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1
Iovenitti, Pio Gioacchino, and piovenitti@swin edu au. "Three-dimensional measurement using a single camera and target tracking." Swinburne University of Technology, 1997. http://adt.lib.swin.edu.au./public/adt-VSWT20060724.151747.
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This thesis involves the development of a three-dimensional measurement system
for digitising the surface of an object. The measurement system consists of a single
camera and a four point planar target of known size. The target is hand held, and is
used to probe the surface of the object being measured. The position of the target is
tracked by the camera, and the contact point on the object is determined. The vision
based digitising technique can be used in the industrial and engineering design fields
during the product development phase.
The accuracy of measurement is an important criterion for establishing the success
of the 3-D measurement system, and the factors influencing the accuracy are
investigated. These factors include the image processing algorithm, the intrinsic
parameters of the camera, the algorithm to determine the position, and various
procedural variables. A new iterative algorithm is developed to calculate position.
This algorithm is evaluated, and its performance is compared to that of an analytic
algorithm. Simple calibration procedures are developed to determine the intrinsic
parameters, and mathematical models are constructed to justify these procedures.
The performance of the 3-D measurement system is established and compared to
that of existing digitising systems.
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Naeem, Asad. "Single and multiple target tracking via hybrid mean shift/particle filter algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12699/.
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This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences.
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Kilic, Varlik. "Performance Improvement Of A 3d Reconstruction Algorithm Using Single Camera Images." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606259/index.pdf.
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In this study, it is aimed to improve a set of image processing techniques used in a previously developed method for reconstructing 3D parameters of a secondary passive target using single camera images. This 3D reconstruction method was developed and implemented on a setup consisting of a digital camera, a computer, and a positioning unit. Some automatic target recognition techniques were also included in the method. The passive secondary target used is a circle with two internal spots.
In order to achieve a real time target detection, the existing binarization, edge detection, and ellipse detection algorithms are debugged, modified, or replaced to increase the speed, to eliminate the run time errors, and to become compatible for target tracking. The overall speed of 20 Hz is achieved for 640x480 pixel resolution 8 bit grayscale images on a 2.8 GHz computer
A novel target tracking method with various tracking strategies is introduced to reduce the search area for target detection and to achieve a detection and reconstruction speed at the maximum frame rate of the hardware.
Based on the previously suggested lens distortion model, distortion measurement, distortion parameters determination, and distortion correction methods for both radial and tangential distortions are developed. By the implementation of this distortion correction method, the accuracy of the 3D reconstruction method is enhanced.
The overall 3D reconstruction method is implemented in an integrated software and hardware environment as a combination of the methods with the best performance among their alternatives. This autonomous and real time system is able to detect the secondary passive target and reconstruct its 3D configuration parameters at a rate of 25 Hz. Even for extreme conditions, in which it is difficult or impossible to detect the target, no runtime failures are observed.
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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.
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Ahmeda, Shubat Senoussi. "Adaptive target tracking algorithms for phased array radar." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336953.
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Lin, Horng-Jyh. "Investigations of manoeuvring target tracking using IMM algorithms." Thesis, University of Sussex, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332662.
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Pitre, Ryan. "A Comparison of Multiple-Model Target Tracking Algorithms." ScholarWorks@UNO, 2004. http://louisdl.louislibraries.org/u?/NOD,168.
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Thesis (M.S.)--University of New Orleans, 2004.Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering."--Thesis t.p. Vita. Includes bibliographical references.
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Nagarajan, Nishatha. "Target Tracking Via Marine Radar." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1345125374.
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Yagoob, Muhammad Moeen. "Computationally efficient algorithms for non-linear target tracking problems." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499109.
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Zhao, Zhanlue. "Performance Appraisal of Estimation Algorithms and Application of Estimation Algorithms to Target Tracking." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/394.
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This dissertation consists of two parts. The first part deals with the performance appraisal of estimation algorithms. The second part focuses on the application of estimation algorithms to target tracking. Performance appraisal is crucial for understanding, developing and comparing various estimation algorithms. In particular, with the evolvement of estimation theory and the increase of problem complexity, performance appraisal is getting more and more challenging for engineers to make comprehensive conclusions. However, the existing theoretical results are inadequate for practical reference. The first part of this dissertation is dedicated to performance measures which include local performance measures, global performance measures and model distortion measure. The second part focuses on application of the recursive best linear unbiased estimation (BLUE) or lineae minimum mean square error (LMMSE) estimation to nonlinear measurement problem in target tracking. Kalman filter has been the dominant basis for dynamic state filtering for several decades. Beyond Kalman filter, a more fundamental basis for the recursive best linear unbiased filtering has been thoroughly investigated in a series of papers by Dr. X. Rong Li. Based on the so-called quasirecursive best linear unbiased filtering technique, the constraints of the Kalman filter Linear-Gaussian assumptions can be relaxed such that a general linear filtering technique for nonlinear systems can be achieved. An approximate optimal BLUE filter is implemented for nonlinear measurements in target tracking which outperforms the existing method significantly in terms of accuracy, credibility and robustness.
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Nguyen, Trang. "Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking." ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/20.
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Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem.
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Wu, Jiande. "Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1953.
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Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task.
Parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and evaluate computationally efficient PF algorithms for target tracking, and thereby bring them closer to practical applications. To reach this goal, a number of parallel PF algorithms is designed and implemented using different parallel hardware architectures such as Computer Cluster, Graphics Processing Unit (GPU), and Field-Programmable Gate Array (FPGA). Proposed is an improved PF implementation for computer cluster - the Particle Transfer Algorithm (PTA), which takes advantage of the cluster architecture and outperforms significantly existing algorithms. Also, a novel GPU PF algorithm implementation is designed which is highly efficient for GPU architectures. The proposed algorithm implementations on different parallel computing environments are applied and tested for target tracking problems, such as space object tracking, ground multitarget tracking using image sensor, UAV-multisensor tracking. Comprehensive performance evaluation and comparison of the algorithms for both tracking and computational capabilities is performed. It is demonstrated by the obtained simulation results that the proposed implementations help greatly overcome the computational issues of particle filtering for realistic practical problems.
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Guner, Onur. "Evaluation Of Multi Target Tracking Algorithms In The Presence Of Clutter." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606406/index.pdf.
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ABSTRACT
EVALUATION OF MULTI TARGET TRACKING ALGORITHMS
IN THE PRESENCE OF CLUTTER
Güner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoglu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 &
M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPDAF) and interactive multiple model filtering are used for this purpose. Keywords: clutter, false alarms, track initiation, data association, gating, target kinematics, IMM, JPDAF
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Matsunaga, Shinichiro. "A single-chip CMOS tracking image sensor for a complex target." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/15285.
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Recently CMOS sensors have been greatly improved, and various smart sensors, which include processing units inside the chip, have been reported. Although a number of methods for motion detection are reported in the literature, little attention has been paid to tracking sensors. Many motion detection sensors have been reported, and sometimes motion detection and motion object tracking are regarded as equivalent, since they typically use the same algorithm at the front end. However they are not the same. Tracking means tracing the progress of objects as they move about in a visual scene. The target must be followed continuously for a long time. On the other hand motion detectors only output instantaneous target movement. There are two main problems in the existing design of tracking sensors. Firstly they cannot handle complex target images, therefore simple features are used as the target for some sensors, even though the target does not always have those features in the real-world. Usually those sensors only track simple features such as edges or bright points. Secondly the precision of tracking is quite poor due to their circuit techniques. So although they perform well on synthetic data, performance is poor on real-world images. This thesis investigates how to realize a single chip tracking sensor which can deal with complex real-world object. A survey of existing tracking algorithms, which can be implemented on silicon is presented. A computation directed algorithm, which is known as BMA (Block Matching Algorithm) has been adopted and modified. This algorithm can deal not only with edges but also with more ambiguous features, and a performance of the algorithm is tested with real-world images. Hybrid circuits consisting sensors, analogue and digital circuits have been developed, and high precision tracking circuits are presented. The circuits, which incorporate 64x64 Active Pixel Sensors, parallel analogue memory and a Switched Capacitor parallel processing unit, are implemented on a single chip and fabricated. The circuits have been tested electrically, and total chip performance has been examined with test bed for tracking. Finally ideas for future improvements are presented. These are actually possible with current CMOS technology.
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Li, Lingjie Luo Zhi-Quan. "Data fusion and filtering for target tracking and identification /." *McMaster only, 2003.
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Bertozzi, Enrico. "Development of Reinforcement Learning Algorithms for Non-cooperative Target Localization and Tracking." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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The problem addressed in this thesis is to use swarm agents to find the optimal placement to reach optimal localization performance of a target node in a wireless sensor network scenario. Localization can be based on simply received signal strength (RSSI) and trilateration. To measure the accuracy of the localization process, geometric dilution of precision (GDOP) has been used. Trilateration is performed by mobile anchors that, in this work, will be supposed to be drones. Three anchors are used. The anchors are free to move in an environment represented by a grid. Each drone can assume a grid cell as location. To move from a cell to another there are five actions allowed. Each agent can move one cell square north, south, east, west or remain in its current position, if possible. Localization is performed on a target node arbitrarily positioned in the environment. Each time drones make a move, a reward is awarded to them depending on the estimated distance from the target and the GDOP. This allows drones to determine whether or not the action taken in a particular cell was valid. Three different algorithms have been proposed and implemented. The first one called 'Multi agent Q-learning' is used in small gridworld. Each executable action in a cell is assigned a certain value, called q-value, indicating how much that action is useful to reach the final goal. The tested scenarios include both environments with and without obstacles. A deep reinforcement learning approach was used to shift the problem even to larger environments. Thanks to the use of neural networks, an algorithm called 'actor-critic' has been implemented. The action will be chosen over a distribution of probabilities. Finally, the two algorithms have been united in a hybrid technique that allows trilateration to be performed even on mobile targets.
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Wahlberg, Fredrik. "Parallel algorithms for target tracking on multi-coreplatform with mobile LEGO robots." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155537.
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The aim of this master thesis was to develop a versatile and reliable experimentalplatform of mobile robots, solving tracking problems, for education and research.Evaluation of parallel bearings-only tracking and control algorithms on a multi-corearchitecture has been performed. The platform was implemented as a mobile wirelesssensor network using multiple mobile robots, each using a mounted camera for dataacquisition. Data processing was performed on the mobile robots and on a server,which also played the role of network communication hub. A major focus was toimplement this platform in a flexible manner to allow for education and futureresearch in the fields of signal processing, wireless sensor networks and automaticcontrol. The implemented platform was intended to act as a bridge between the idealworld of simulation and the non-ideal real world of full scale prototypes.The implemented algorithms did estimation of the positions of the robots, estimationof a non-cooperating target's position and regulating the positions of the robots. Thetracking algorithms implemented were the Gaussian particle filter, the globallydistributed particle filter and the locally distributed particle filter. The regulator triedto move the robots to give the highest possible sensor information under givenconstraints. The regulators implemented used model predictive control algorithms.Code for communicating with filters in external processes were implementedtogether with tools for data extraction and statistical analysis.Both implementation details and evaluation of different tracking algorithms arepresented. Some algorithms have been tested as examples of the platformscapabilities, among them scalability and accuracy of some particle filtering techniques.The filters performed with sufficient accuracy and showed a close to linear speedupusing up to 12 processor cores. Performance of parallel particle filtering withconstraints on network bandwidth was also studied, measuring breakpoints on filtercommunication to avoid weight starvation. Quality of the sensor readings, networklatency and hardware performance are discussed. Experiments showed that theplatform was a viable alternative for data acquisition in algorithm development and forbenchmarking to multi-core architecture. The platform was shown to be flexibleenough to be used a framework for future algorithm development and education inautomatic control.
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Xiao, Jingjing. "Single-target tracking of arbitrary objects using multi-layered features and contextual information." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/6688/.
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This thesis investigated single-target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as significant deformations of the target, occlusions, illumination variations, background clutter and camouflage. To achieve robust tracking performance under these severe conditions, this thesis proposed firstly a novel RGB single-target tracker which models the target with multi-layered features and contextual information. The proposed algorithm was tested on two different tracking benchmarks, i.e., VTB and VOT, where it demonstrated significantly more robust performance than other state-of-the-art RGB trackers. Proposed secondly was an extension of the designed RGB tracker to handle RGB-D images using both temporal and spatial constraints to exploit depth information more robustly. For evaluation, the thesis introduced a new RGB-D benchmark dataset with per-frame annotated attributes and extensive bias analysis, on which the proposed tracker achieved the best results. Proposed thirdly was a new tracking approach to handle camouflage problems in highly cluttered scenes exploiting global dynamic constraints from the context. To evaluate the tracker, a benchmark dataset was augmented with a new set of clutter sub-attributes. Using this dataset, it was demonstrated that the proposed method outperforms other state-of-the-art single target trackers on highly cluttered scenes.
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Donnelly, Kieran. "Development of a test suite for single object tracking algorithms in video." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33645.
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Flying Camera Solutions (FlyCam), within Sony Lund's startup accelerator, intends to provide drone videography to paying customers in ski resorts: a customer should be able to go about their activity as usual while a drone films them. Visual object tracking, enabling the drone to track the customer throughout the activity, is a primary obstacle in creating a viable autonomous videography service. FlyCam needs an object tracking algorithm which is accurate, robust, real-time, and requiring minimal computational overhead. We propose two innovations to aid in the selection of an appropriate tracking algorithm. Firstly, a video annotation algorithm, making use of an object detector to record the position and type of object in each frame of a video clip. Secondly, an algorithm designed to evaluate the performance of any given object tracker based on a set of performance metrics. These metrics include, among others, measures of positional accuracy, frame rate, and false positive rate. For the video annotation algorithm we implemented the state-of-the-art Mask R-CNN object detector, which achieved an average frame rate of 1.5 fps annotating video clips in up to 4K resolution. Another algorithm then played back the annotated clips to the user such that incorrect object detections could be rooted out or rectified. With little relevant annotated video available, the annotation algorithm proved useful in preparing a suite of 18 clips to be evaluated. Ten performance metrics were adapted from multi-object to single-object tracking. Nine tracking algorithms were then run on each of the 18 test video clips at varying resolutions to produce 375 tracking observations for analysis. The evaluation results revealed the optimal tracking algorithm to be Re3: a recurrent-convolutional neural network tracker which runs at respectable speeds on a consumer laptop. This is a promising result; with enough annotated data, neural networks can be retrained to improve performance. Within just a few months of operation, FlyCam could amass enough specific video data to significantly improve the neural network-based tracker.
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Arif, Omar. "Robust target localization and segmentation using statistical methods." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.
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This thesis aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods, which map the data to a higher dimensional space. A pre-image framework is provided to find the mapping from the embedding space to the input space for several manifold learning and dimensional learning algorithms. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. This framework is extended to incorporate the background information in an energy based formulation, which is minimized using graph cut and to track multiple objects using a single learned model. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods.
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Hadzagic, Melita. "Comparative analysis of the IMM-JVC and the IMM-JPDA algorithms for multiple-target tracking." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32959.
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When tracking closely maneuvering targets, the critical role is played by both the chosen method of data association and the target-tracking algorithm. Without an effective association, state estimation is at risk. Without an efficient state prediction, the performance of an associator can be degraded. In developing an assignment strategy the crucial issue is whether to assign a track or observation as belonging uniquely to another observation or track, or to allow a track to be associated non-uniquely with multiple candidate observations.This thesis presents a comparative study of two assignment alternatives, namely the NC (unique association of a measurement to an existing track) and JPDA (nonunique association of a measurement to an existing track) algorithms. These assignment strategies were combined with an Interacting Multiple Model (IMM) positional estimator, which superiority over the other single scan algorithms has been largely documented. The respective tracking performance of the IMM-JVC and EV1M-JPDAF algorithms for multiple target tracking has been evaluated. After a detailed description of the IMM-JVC and IMM-JPDAF formalisms, and the IMM-JPDAF implementation issues, an analysis of the results of NC association compared to JPDA association is presented. Simulation results obtained on two scenarios involving two closely maneuvering aircraft confirm the superiority of the IMM-JVC.
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El-Mahy, Mohamed Kamel Sayed Ahmed. "An investigation into Kalman filter target tracking algorithms and their real time parallel transputer implementation." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358825.
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This thesis reviews the applications of Kalman filtering estimation to the problem of target tracking. Both linear and nonlinear forms of Kalman filter are reviewed and models of target manoeuvre discussed. Manoeuvre adaptation schemes are examined to detect the onset and completion of manoeuvres. Target manoeuvre coordinates are also examined and a new target model proposed which significantly improves tracking performance. The new model includes turn rate estimation. The real-time implementation of tracking Kalman filters is also studied both for a simple processor and a multiple processor architecture. Tracking algorithms are coded in Parallel C and evaluated for speed and efficiency
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Keaikitse, Advice Seiphemo. "Long-term tracking of multiple interacting pedestrians using a single camera." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86632.
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Thesis (MSc)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Object detection and tracking are important components of many computer vision applications including automated surveillance. Automated surveillance attempts to solve the challenges associated with closed-circuit camera systems. These include monitoring large numbers of cameras and the associated labour costs, and issues related to targeted surveillance. Object detection is an important step of a surveillance system and must overcome challenges such as changes in object appearance and illumination, dynamic background objects like ickering screens, and shadows. Our system uses Gaussian mixture models, which is a background subtraction method, to detect moving objects. Tracking is challenging because measurements from the object detection stage are not labelled and could be from false targets. We use multiple hypothesis tracking to solve this measurement origin problem. Practical long-term tracking of objects should have re-identi cation capabilities to deal with challenges arising from tracking failure and occlusions. In our system each tracked object is assigned a one-class support vector machine (OCSVM) which learns the appearance of that object. The OCSVM is trained online using HSV colour features. Therefore, objects that were occluded or left the scene can be reidenti ed and their tracks extended. Standard, publicly available data sets are used for testing. The performance of the system is measured against ground truth using the Jaccard similarity index, the track length and the normalized mean square error. We nd that the system performs well.
AFRIKAANSE OPSOMMING: Die opsporing en volging van voorwerpe is belangrike komponente van baie rekenaarvisie toepassings, insluitend outomatiese bewaking. Outomatiese bewaking poog om die uitdagings wat verband hou met geslote kring kamera stelsels op te los. Dit sluit in die monitering van groot hoeveelhede kameras en die gepaardgaande arbeidskoste, en kwessies wat verband hou met toegespitse bewaking. Die opsporing van voorwerpe is 'n belangrike stap in 'n bewakingstelsel en moet uitdagings soos veranderinge in die voorwerp se voorkoms en beligting, dinamiese agtergrondvoorwerpe soos ikkerende skerms, en skaduwees oorkom. Ons stelsel maak gebruik van Gaussiese mengselmodelle, wat 'n agtergrond-aftrek metode is, om bewegende voorwerpe op te spoor. Volging is 'n uitdaging, want afmetings van die voorwerp-opsporing stadium is nie gemerk nie en kan afkomstig wees van valse teikens. Ons gebruik verskeie hipotese volging (multiple hypothesis tracking ) om hierdie meting-oorsprong probleem op te los. Praktiese langtermynvolging van voorwerpe moet heridenti seringsvermoëns besit, om die uitdagings wat voortspruit uit mislukte volging en okklusies te kan hanteer. In ons stelsel word elke gevolgde voorwerp 'n een-klas ondersteuningsvektormasjien (one-class support vector machine, OCSVM) toegeken, wat die voorkoms van daardie voorwerp leer. Die OCSVM word aanlyn afgerig met die gebruik van HSV kleurkenmerke. Daarom kan voorwerpe wat verdwyn later her-identi seer word en hul spore kan verleng word. Standaard, openbaar-beskikbare datastelle word vir toetse gebruik. Die prestasie van die stelsel word gemeet teen korrekte afvoer, met behulp van die Jaccard ooreenkoms-indeks, die spoorlengte en die genormaliseerde gemiddelde kwadraatfout. Ons vind dat die stelsel goed presteer.
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Tippetts, Beau J. "Real-time implementations of vision algorithms for control, stabilization, and target tracking, for a hovering micro-UAV /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2374.pdf.
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Ege, Emre. "A Comparative Study Of Tracking Algorithms In Underwater Environment Using Sonar Simulation." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608866/index.pdf.
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Target tracking is one the most fundamental elements of a radar system. The aim of
target tracking is the reliable estimation of a target's true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment and the associated differences in the corresponding sensors, the successful use of similar principles and techniques in an underwater scenario is still an active topic of research. A comparative study of the effects of the underwater environment on a number of tracking algorithms is the focus of the present thesis. The tracking algorithms inspected are: the Kalman Filter, the Extended Kalman Filter and the Particle Filter. We also investigate in particular the IMM extension to KF and EKF filters. These algorithms are tested under several underwater environment scenarios.
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Lee, Jehoon. "Statistical and geometric methods for visual tracking with occlusion handling and target reacquisition." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43582.
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Computer vision is the science that studies how machines understand scenes and automatically make decisions based on meaningful information extracted from an image or multi-dimensional data of the scene, like human vision. One common and well-studied field of computer vision is visual tracking. It is challenging and active research area in the computer vision community. Visual tracking is the task of continuously estimating the pose of an object of interest from the background in consecutive frames of an image sequence. It is a ubiquitous task and a fundamental technology of computer vision that provides low-level information used for high-level applications such as visual navigation, human-computer interaction, and surveillance system.
The focus of the research in this thesis is visual tracking and its applications. More specifically, the object of this research is to design a reliable tracking algorithm for a deformable object that is robust to clutter and capable of occlusion handling and target reacquisition in realistic tracking scenarios by using statistical and geometric methods. To this end, the approaches developed in this thesis make extensive use of region-based active contours and particle filters in a variational framework. In addition, to deal with occlusions and target reacquisition problems, we exploit the benefits of coupling 2D and 3D information of an image and an object.
In this thesis, first, we present an approach for tracking a moving object based on 3D range information in stereoscopic temporal imagery by combining particle filtering and geometric active contours. Range information is weighted by the proposed Gaussian weighting scheme to improve segmentation achieved by active contours. In addition, this work present an on-line shape learning method based on principal component analysis to reacquire track of an object in the event that it disappears from the field of view and reappears later. Second, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose in 3D space. In this work, we take advantage of knowledge of a 3D model of an object and we employ particle filtering to generate and propagate the translation and rotation parameters in a decoupled manner. Moreover, to continuously track the object in the presence of occlusions, we propose an occlusion detection and handling scheme based on the control of the degree of dependence between predictions and measurements of the system. Third, we introduce the fast level-set based algorithm applicable to real-time applications. In this algorithm, a contour-based tracker is improved in terms of computational complexity and the tracker performs real-time curve evolution for detecting multiple windows. Lastly, we deal with rapid human motion in context of object segmentation and visual tracking. Specifically, we introduce a model-free and marker-less approach for human body tracking based on a dynamic color model and geometric information of a human body from a monocular video sequence. The contributions of this thesis are summarized as follows:
1. Reliable algorithm to track deformable objects in a sequence consisting of 3D range data by combining particle filtering and statistics-based active contour models.
2. Effective handling scheme based on object's 2D shape information for the challenging situations in which the tracked object is completely gone from the image domain during tracking.
3. Robust 2D-3D pose tracking algorithm using a 3D shape prior and particle filters on SE(3).
4. Occlusion handling scheme based on the degree of trust between predictions and measurements of the tracking system, which is controlled in an online fashion.
5. Fast level set based active contour models applicable to real-time object detection.
6. Model-free and marker-less approach for tracking of rapid human motion based on a dynamic color model and geometric information of a human body.
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Sitanayah, Lanny. "Finding boundary cycles in location-free low density wireless sensor networks for mobile target tracking." University of Western Australia. School of Computer Science and Software Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0158.
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Wireless Sensor Networks (WSNs) comprise a large number of sensor nodes, which are spread out within a region and communicate using wireless links. In some WSN applications, recognizing boundary nodes is important for topology discovery, geographic routing and tracking. In this thesis, we study the problem of identifying the boundary nodes of a WSN. In a WSN, close-by nodes can communicate with their neighbors and have the ability to estimate distances to nearby nodes, but not necessarily the true distances. Our objective is to find the boundary nodes by using the connectivity relation and neighbor distance information without any knowledge of node locations. Moreover, our main aim is to design a distributed algorithm that works even when the average degree is low. We propose a heuristic algorithm to find the boundary nodes which are connected in a boundary cycle of a location-free, low density, randomly deployed WSN. We develop the key ideas of our boundary detection algorithm in the centralized scenario and extend these ideas to the distributed scenario. Then, we show by simulation experiments that the distributed implementation outperforms the centralized one. The centralized implementation relies on the connectivity of the network to the base station. Therefore, for low density disconnected networks, the algorithm cannot find boundaries in partitions of the network that cannot establish connection to the base station. This condition leads to a low quality of boundary discovery. In contrast, the distributed implementation is more realistic for real WSNs, especially for relatively sparse networks when all local information cannot be collected very well due to sparse connectivity. In low-degree disconnected networks, the simulation results show that the distributed implementation has a higher quality of boundaries compared to the centralized implementation.
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Oh, Seung-Min. "Nonlinear Estimation for Vision-Based Air-to-Air Tracking." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19882.
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Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning.
As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing.
In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation.
In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed.
A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.
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Deneault, Dustin. "Tracking ground targets with measurements obtained from a single monocular camera mounted on an Unmanned Aerial Vehicle." Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/528.
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Valmori, Filippo. "UWB radar sensor networks: Detection algorithms design and experimental analysis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10164/.
Full textAbstract:
In the last years radar sensor networks for localization and tracking in indoor environment have generated more and more interest, especially for anti-intrusion security systems. These networks often use Ultra Wide Band (UWB) technology, which consists in sending very short (few nanoseconds) impulse signals. This approach guarantees high resolution and accuracy and also other advantages such as low price, low power consumption and narrow-band interference (jamming) robustness. In this thesis the overall data processing (done in MATLAB environment) is discussed, starting from experimental measures from sensor devices, ending with the 2D visualization of targets movements over time and focusing mainly on detection and localization algorithms. Moreover, two different scenarios and both single and multiple target tracking are analyzed.
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Bjering, Beatrice. "Estimations of 3D velocities from a single camera view in ice hockey." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254320.
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Ice hockey is a contact sport with a high risk of brain injuries such as concussions. This is a serious health concern and there is a need of better understanding of the relationship between the kinematics of the head and concussions. The velocity and the direction of impact are factors that might affect the severity of the concussions. Therefore the understanding of concussions can be improved by extracting velocities from video analysis. In this thesis a prototype to extract 3D velocities from one single camera view was developed by using target tracking algorithms and homography. A validation of the method was done where the mean error was estimated to 21.7%. The prototype evaluated 60 cases of tackles where 30 resulted in concussions and the other 30 tackles did not result in concussions. No significant difference in the velocities between the two groups could be found. The mean velocity for the tackles that resulted in concussions were 6.55 m/s for the attacking player and 4.59 m/s for the injured player. The prototype was also compared with velocities extracted through SkillSpector from a previous bachelor thesis. There was a significant difference between the velocities compiled with SkillSpector and the developed prototype in this thesis. A validation of SkillSpector was also made, which showed that it had a mean error of 37.4%.Ishockey är en kontaktsport med hög risk för hjärnskador, så som hjärnskakningar. Detta är ett stort hälsoproblem och det finns ett behov av större förståelse mellan huvudets kinematik och hjärnskakningar. Hastigheten och riktningen av kollisionerna är faktorer som kan påverka svårighetsgraden av hjärnskakningarna. Därför kan förståelsen av hjärnskakningar förbättras genom att extrahera hastigheter med videoanalys. I denna rapport utvecklades en prototyp för att ta fram 3D hastigheter från en kameravinkel genom att använda målsökningsalgoritmer och homografi. En validering av prototypen gjordes där medelfelet uppskattades till 21.7%. Prototypen utvärderade även 60 fall av tacklingar där 30 resulterade hjärnskakningar och där de andra 30 tacklingarna inte resulterade i hjärnskakningar. Ingen signifikant skillnad mellan de två grupperna kunde påvisas. Medelhastigheten för tacklingarna som resulterade i hjärnskakning var 6.55 m/s för den attackerande spelaren och 4.59 m/s för den skadade spelaren. Prototypen jämfördes också med hastigheter som tagits fram med SkillSpector i ett tidigare kandidatexamensarbete. Det var en signifikant skillnad mellan de hastigheter som togs fram med prototypen och de som tog fram med SkillSpector. En validering av SkillSpector gjordes också, som visade att medelfelet var 37.4%.
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Vo, Ba Tuong. "Random finite sets in Multi-object filtering." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0045.
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[Truncated abstract] The multi-object filtering problem is a logical and fundamental generalization of the ubiquitous single-object vector filtering problem. Multi-object filtering essentially concerns the joint detection and estimation of the unknown and time-varying number of objects present, and the dynamic state of each of these objects, given a sequence of observation sets. This problem is intrinsically challenging because, given an observation set, there is no knowledge of which object generated which measurement, if any, and the detected measurements are indistinguishable from false alarms. Multi-object filtering poses significant technical challenges, and is indeed an established area of research, with many applications in both military and commercial realms. The new and emerging approach to multi-object filtering is based on the formal theory of random finite sets, and is a natural, elegant and rigorous framework for the theory of multiobject filtering, originally proposed by Mahler. In contrast to traditional approaches, the random finite set framework is completely free of explicit data associations. The random finite set framework is adopted in this dissertation as the basis for a principled and comprehensive study of multi-object filtering. The premise of this framework is that the collection of object states and measurements at any time are treated namely as random finite sets. A random finite set is simply a finite-set-valued random variable, i.e. a random variable which is random in both the number of elements and the values of the elements themselves. Consequently, formulating the multiobject filtering problem using random finite set models precisely encapsulates the essence of the multi-object filtering problem, and enables the development of principled solutions therein. '...' The performance of the proposed algorithm is demonstrated in simulated scenarios, and shown at least in simulation to dramatically outperform traditional single-object filtering in clutter approaches. The second key contribution is a mathematically principled derivation and practical implementation of a novel algorithm for multi-object Bayesian filtering, based on moment approximations to the posterior density of the random finite set state. The performance of the proposed algorithm is also demonstrated in practical scenarios, and shown to considerably outperform traditional multi-object filtering approaches. The third key contribution is a mathematically principled derivation and practical implementation of a novel algorithm for multi-object Bayesian filtering, based on functional approximations to the posterior density of the random finite set state. The performance of the proposed algorithm is compared with the previous, and shown to appreciably outperform the previous in certain classes of situations. The final key contribution is the definition of a consistent and efficiently computable metric for multi-object performance evaluation. It is shown that the finite set theoretic state space formulation permits a mathematically rigorous and physically intuitive construct for measuring the estimation error of a multi-object filter, in the form of a metric. This metric is used to evaluate and compare the multi-object filtering algorithms developed in this dissertation.
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Souza, Éfren Lopes de. "Algoritmos para rastreamento de alvos em áreas quantizadas com redes de sensores sem fio." Universidade Federal do Amazonas, 2014. http://tede.ufam.edu.br/handle/tede/4154.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Target tracking in Wireless Sensor Networks (WSNs) is an application in which the nodes cooperate to estimate the position of one or more objects of interest. In this context, the contributions of this work are fourfold. First, a survey the state-of-the-art about target tracking algorithms, in which we identified three formulations of tracking problem, and we classified them according to their characteristics. Furthermore, we divided the target tracking process in components to make the general understanding easier. Second, we propose and evaluate the PRATIQUE algorithm for tracking animals in forests. In this case, the nodes are organized into a grid to make feasible the use of sensor nodes in this kind of area in such a way that each cell of the grid is a region that can be occupied by the target. The algorithm estimates the cell where the target is, and uses predictions and hybrid clustering to reduce the communication cost and ensure the tracking accuracy. The results of the simulations show that prediction errors are approximately one cell. The third contribution is the TATI algorithm, this algorithm guides a tracker to approach the target. The sensor network is organized into faces to make the cooperation among the nodes easier, and reduce the path between the tracker and the target. The results show that energy consumption is reduced by 15%, and the tracker stays about 10m closer to the target, compared to the baseline. The fourth contribution is a scheme for performing localization and tracking tasks simultaneously in such a way that errors of range-based localization algorithms are reduced. This algorithm takes advantage of the messages sent to track the target to filter the noise in the distance estimation, reducing localization errors while tracking. The results show that the localization errors can be reduced by up to 70%.
Rastreamento de alvos em Redes de Sensores Sem Fio (RSSFs) é um tipo de aplicação em que os nós cooperam para estimar a posição de um ou mais objetos de interesse. Nesse contexto, este trabalho possui quatro contribuições. A primeira contribuição é um levantamento bibliográfico do estado-da-arte, em que identificamos três diferentes formulações de rastreamento e as classificamos de acordo com suas características. Além disso, dividimos o processo de rastreamento em componentes para facilitar o entendimento geral. A segunda contribuição é a elaboração e avaliação do algoritmo PRATIQUE para rastrear animais em florestas. Nesse caso, os nós são organizados em grade para viabilizar a utilização dos nós sensores nesse tipo de área, de forma que cada célula da grade é uma região que pode ser ocupada pelo alvo. O algoritmo estima a célula em que o alvo está, e usa previsão e um esquema híbrido de agrupamento para reduzir o custo de comunicação e garantir a precisão do rastreamento. Os resultados das simulações mostram que os erros de previsão são de aproximadamente uma célula. A terceira contribuição é o algoritmo TATI, esse algoritmo guia um objeto que visa alcançar o alvo. A rede é estruturada em faces para facilitar a cooperação entre os nós e reduzir o caminho entre o objeto guiado e o alvo. Os resultados mostram que o consumo de energia é reduzido em 15% e o objeto guiado fica cerca de 10m mais próximo do alvo, se comparado com a abordagem relacionada. A quarta contribuição é um esquema para executar as tarefas de localização e rastreamento simultaneamente para reduzir os erros dos algoritmos de localização baseados em alcance. As mensagens enviadas para rastrear o alvo são aproveitadas para filtrar os ruídos presentes nas estimativas de distância, reduzindo o erro de localização enquanto o rastreamento ocorre. Os resultados mostram que os erros de localização podem ser reduzidos em até 70%.
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Brégère, Margaux. "Stochastic bandit algorithms for demand side management Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders Online Hierarchical Forecasting for Power Consumption Data Target Tracking for Contextual Bandits : Application to Demand Side Management." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM022.
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L'électricité se stockant difficilement à grande échelle, l'équilibre entre la production et la consommation doit être rigoureusement maintenu. Une gestion par anticipation de la demande se complexifie avec l'intégration au mix de production des énergies renouvelables intermittentes. Parallèlement, le déploiement des compteurs communicants permet d'envisager un pilotage dynamique de la consommation électrique. Plus concrètement, l'envoi de signaux - tels que des changements du prix de l'électricité – permettrait d'inciter les usagers à moduler leur consommation afin qu'elle s'ajuste au mieux à la production d'électricité. Les algorithmes choisissant ces signaux devront apprendre la réaction des consommateurs face aux envois tout en les optimisant (compromis exploration-exploitation). Notre approche, fondée sur la théorie des bandits, a permis de formaliser ce problème d'apprentissage séquentiel et de proposer un premier algorithme pour piloter la demande électrique d'une population homogène de consommateurs. Une borne supérieure d'ordre T⅔ a été obtenue sur le regret de cet algorithme. Des expériences réalisées sur des données de consommation de foyers soumis à des changements dynamiques du prix de l'électricité illustrent ce résultat théorique. Un jeu de données en « information complète » étant nécessaire pour tester un algorithme de bandits, un simulateur de données de consommation fondé sur les auto-encodeurs variationnels a ensuite été construit. Afin de s'affranchir de l'hypothèse d'homogénéité de la population, une approche pour segmenter les foyers en fonction de leurs habitudes de consommation est aussi proposée. Ces différents travaux sont finalement combinés pour proposer et tester des algorithmes de bandits pour un pilotage personnalisé de la consommation électriqueAs electricity is hard to store, the balance between production and consumption must be strictly maintained. With the integration of intermittent renewable energies into the production mix, the management of the balance becomes complex. At the same time, the deployment of smart meters suggests demand response. More precisely, sending signals - such as changes in the price of electricity - would encourage users to modulate their consumption according to the production of electricity. The algorithms used to choose these signals have to learn consumer reactions and, in the same time, to optimize them (exploration-exploration trade-off). Our approach is based on bandit theory and formalizes this sequential learning problem. We propose a first algorithm to control the electrical demand of a homogeneous population of consumers and offer T⅔ upper bound on its regret. Experiments on a real data set in which price incentives were offered illustrate these theoretical results. As a “full information” dataset is required to test bandit algorithms, a consumption data generator based on variational autoencoders is built. In order to drop the assumption of the population homogeneity, we propose an approach to cluster households according to their consumption profile. These different works are finally combined to propose and test a bandit algorithm for personalized demand side management
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Beltrán, Diego Fernando Burgos. "Algoritmos genéticos compactados para estimação de direção de chegada e conformação de feixe num arranjo de antenas em ambiente CDMA." Universidade Federal de Goiás, 2015. http://repositorio.bc.ufg.br/tede/handle/tede/6027.
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Outro
The continuous technological advances in the areas of electronics and programming made the signal processing techniques much easier to implement, allowing them to be incorporated in the communication systems, improving their performance. This work approaches the problem of estimating direction of arrival or angle of incidence (DOA) of electromagnetic wave fronts of a linear antenna array, and of beamforming of the array. Among the various techniques that exist in the literature, the Least Mean Squared algorithm (LMS) is a deterministic method that stands out for its simplicity, ease of implementation and the tendency to find local minima. On the other hand, the Genetic Algorithm (GA) is a heuristic method that ensures more comprehensive exploration possibilities avoiding the tendency of sticking to local minima, but offering greater difficulty of implementation, and higher computational complexity. The recently proposed Compact Genetic Algorithm (cGA) is a tool that shares all the virtues of GA, but without requiring the large computational cost that a GA entails. Since this method has not yet been used for controlling antenna arrays, this paper proposes to use it as the estimation of DOA and beamforming, in addition to enhance it with a number of modifications to make it more robust and more complete, though making it computationally heavier. This work presents simulations where the proposed adaptive receiver is evaluated under different scenarios of signal to noise ratio (SNR), number of interfering sources and convergence velocity. Moreover, moving users tracking situations are simulated, where the receiver's ability to adapt its radiation pattern is tested. All tests were done in the code division multiple access (CDMA) environment, where the only information available to the receiver are the sources spreading codes. To verify the operation of the cGA, its performance was compared with that of the LMS algorithm simulation under the same simulation conditions. The development of this thesis allowed to publish the articles named Adaptive Beamforming for Moving Targets Using Genetic Algorithms and a CDMA Reference Signal in the IEEE Colombian Conference on Communications and Computing COLCOM 2015, and Adaptive Beamforming for Moving Targets Using Genetic Algorithms in the IEEE Workshop on Engineering Applications WEA 2015 – International Congress on Engineering. The last one was accepted as an extended version to be publish in the magazine INGENIERÍA that belongs to the Distrital Francisco José de Caldas University in Bogotá, Colombia.
Os contínuos avanços tecnológicos nas áreas da eletrônica e da programação tornaram as técnicas de processamento de sinais muito mais fáceis de implementar, permitindo a incorporação delas nos sistemas de comunicação, melhorando a performance destes. Neste trabalho desenvolve-se o problema de estimação da direção de chegada ou ângulo de incidência (DOA) de frentes de ondas eletromagnéticas sobre um arranjo linear de antenas, além da conformação de feixe (beamforming) do arranjo. Dentre as diversas técnicas existentes na literatura, o algoritmo de Mínima Média Quadrática (LMS, do inglês Least Mean Squared) é um método determinístico que se destaca por sua simplicidade, facilidade de implementação e a tendência de encontrar mínimos locais como resposta. Por outro lado, o Algoritmo Genético (AG) é um método heurístico que garante uma exploração mais completa de possibilidades evitando a tendência de cair em mínimos locais, mas oferecendo uma maior dificuldade de implementação, além de maior complexidade computacional. Recentemente, foi proposto o Algoritmo Genético Compacto (AGC), que é uma ferramenta que compartilha todas as virtudes dos Algoritmos Genéticos, porém sem exigir o grande custo computacional que um AG implica. Como este método ainda não foi utilizado para o controle de arranjos de antenas, este trabalho propõe utilizá-lo na estimação da DOA e beamforming, além de agregar-lhe uma série de modificações a fim de torná-lo mais robusto e mais completo, apesar de computacionalmente mais pesado. Neste trabalho exibe-se simulações em que o receptor adaptativo proposto é avaliado sob diferentes situações de relação sinal ruído (SNR), quantidade de fontes interferentes e velocidade de convergência. Além disso, simulam-se situações de rastreamento de usuários em movimento, onde é posta à prova a capacidade do receptor adaptar seu diagrama de radiação. Todos os testes foram feitos no ambiente de multiplicidade de acesso via divisão por códigos (CDMA), onde a única informação disponível no receptor são os códigos de espalhamento das fontes. Para conferir o funcionamento do AGC, comparou-se seu desempenho com aquele do algoritmo LMS sob as mesmas condições de simulação. O desenvolvimento desta tese permitiu a publicação dos artigos Adaptive Beamforming for Moving Targets Using Genetic Algorithms and a CDMA Reference Signal no IEEE Colombian Conference on Communications and Computing COLCOM 2015 e Adaptive Beamforming for Moving Targets Using Genetic Algorithms no IEEE Workshop on Engineering Applications WEA 2015 – International Congress on Engineering, este ultimo foi aceito para ser publicado como uma versão estendida na revista INGENIERÍA da universidade Distrital Francisco José de Caldas de Bogotá, Colômbia.
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Fayolle, Jacques. "Etude d'algorithmes de traitement d'images pour l'analyse du mouvement d'objets déformables : application à la mesure de vitesses d'écoulements." Saint-Etienne, 1996. https://tel.archives-ouvertes.fr/tel-00381025.
Full textAbstract:
Nous proposons dans cette thèse des algorithmes de traitement d'images permettant de déterminer les déplacements et déformations locales d'un objet (éventuellement non rigide) au cours du temps. La principale application de ces travaux est l'étude des vitesses au sein d'écoulements fluides, en particulier dans le cas d'écoulements turbulents. Deux approches sont détaillées dans ce mémoire : 1- la vélocimétrie par images de particules, pour laquelle nous introduisons deux techniques novatrices complémentaires des techniques de corrélation : l'identification des déplacements et le cepstre. L'intérêt de ces méthodes est de pouvoir mesurer la distribution des déplacements autour du déplacement moyen. Nous présentons une analyse comparative des domaines d'application de ces méthodes de vélocimétrie par images de particules. 2- La vélocimétrie par suivi de frontières. Cette technique est fondée sur les propriétés de la transformée en ondelettes continue. Nous introduisons un algorithme de caractérisation de singularités filtrées qui nous permet de mesurer la longueur et l'amplitude des singularités d'un signal. Une application de cet algorithme est la détection de points caractéristiques sur des images en niveaux de gris. La mesure du mouvement par suivi de frontières est alors réalisée en mettant en correspondance ces points caractéristiques entre images successives selon un critère de phase du vecteur gradient. Dans la troisième partie de ce travail, l'erreur de mesure réalisée par les deux types de techniques est quantifiée sur un écoulement de Poiseuille. Enfin, nous proposons quelques applications des algorithmes introduits à la mesure de vitesses d'écoulements. Nous avons ainsi pu valider des modèles empiriques de taux de décroissance de l'intensité de turbulence pour une turbulence de grille, ainsi que la vitesse de pénétration d'un jet diesel
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Shiva, Kumar K. A. "Distributed Target Tracking in Camera Networks." Thesis, 2018. http://etd.iisc.ac.in/handle/2005/4154.
Full textAbstract:
Recently, there is a tremendous increase in usage of multi-camera set-up in many applications
such as surveillance, smart homes, sports analysis etc. Since manual analysis of videos in
multi-camera set-up is tedious and inefficient, there is a need to develop automatic computer
algorithms to analyze and understand the videos. In many of the applications mentioned above
target tracking plays a crucial role. Tracking is the process of following a target continuously and
consistently throughout the camera network.
The first part of the thesis develops distributed (where cameras co-operatively work together)
single and multiple target tracking algorithms for overlapping camera networks. The target
tracking problem is modeled as dynamic state estimation problem and uses sigma-point
information filters with probabilistic data association to estimate the state of the target. A
complete distributed algorithm is developed by integrating sigma-point filters with average
consensus algorithm. For multiple targets, to deal with measurement uncertainty, we introduce
measurement-to-measurement association preceding state estimation where we use homography
constraints.
In the second part of the thesis we consider target tracking in non-overlapping camera networks.
Target tracking in non-overlapping camera networks involves two stages: intra and inter-camera
(re-identification) tracking. We identify the key differences between traditional re-identification
problem and re-identification in tracking applications. The re-identification in tracking is
different and challenging compared to traditional re-identification due to: I) The open-set nature
of the gallery II) Dynamic and smaller gallery set III) Rank-1 performance demand and IV)
Multi-camera set-up. A novel evaluation protocol for re-identification for tracking applications is
proposed considering the above mentioned special characteristics. Also, we propose an on-line
update scheme of a metric learning algorithm (KISSME - Keep It Simple and Straight forward
MEtric), to improve the re-identification performance.
Finally, we consider person-of-interest (PoI) tracking algorithm in non-overlapping camera
networks. In PoI tracking a single person is chosen among many persons in a camera and the task
is to track the PoI continuously and consistently across the camera network. We propose two
solutions, one using person-specific metric and other using person-specific features (using
Recurrent Neural Networks).
The effectiveness of the proposed algorithms is demonstrated through real-world data
experiments.
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(8052491), Do hyeung Kim. "MULTI-TARGET TRACKING ALGORITHMS FOR CLUTTERED ENVIRONMENTS." Thesis, 2019.
Find full textAbstract:
Multi-target tracking (MTT) is the problem to simultaneously estimate the number of targets and their states or trajectories. Numerous techniques have been developed for over 50 years, with a multitude of applications in many fields of study; however, there are two most widely used approaches to MTT: i) data association-based traditional algorithms; and ii) finite set statistics (FISST)-based data association free Bayesian multi-target filtering algorithms. Most data association-based traditional filters mainly use a statistical or simple model of the feature without explicitly considering the correlation between the target behavior
and feature characteristics. The inaccurate model of the feature can lead to divergence of the estimation error or the loss of a target in heavily cluttered and/or low signal-to-noise ratio environments. Furthermore, the FISST-based data association free Bayesian multi-target filters can lose estimates of targets frequently in harsh environments mainly
attributed to insufficient consideration of uncertainties not only measurement origin but also target's maneuvers.
To address these problems, three main approaches are proposed in this research work: i) new feature models (e.g., target dimensions) dependent on the target behavior
(i.e., distance between the sensor and the target, and aspect-angle between the longitudinal axis of the target and the axis of sensor line of sight); ii) new Gaussian mixture probability hypothesis density (GM-PHD) filter which explicitly considers the uncertainty in the measurement origin; and iii) new GM-PHD filter and tracker with jump Markov system models. The effectiveness of the analytical findings is demonstrated and validated with illustrative target tracking examples and real data collected from the surveillance radar.
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Chang, Hao-Yen, and 張皓衍. "Moving Target Tracking Using Weighted Multiple Model Algorithms." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/06284859266416871434.
Full textAbstract:
碩士國立高雄第一科技大學
電腦與通訊工程研究所
101
In moving target tracking, a basic Kalman filter (KF) can not track maneuvering moving target effectively. In order to achieve accurate tracking different modes, the multiple model Kalman filter (MMKF) and weighted multiple model algorithm (WMMA) are used to combine stationary, linear motion and acceleration modes. For a target with certain mobile pattern, the algorithm should be capable of selecting the desired Kalman filter from the three Kalman filters to conform the current moving pattern of the moving target. The choice of the desirable Kalman filter depends on the model weights calculated by weighted multiple model algorithm (WMMA). However, the weight calculation in the general WMMA is possibly influenced by the components of the estimated value, causing the model weight errors. The model weight errors will seriously affect the results of estimation. In this thesis, in order to reduce the location error on moving target tracking,we propose a new WMMA to calculate the model weights. We use a new operation mechanism to calculate model weights. The estimated location value for each model is divided into three independent components for calculation of the weights. The model weights on different coordinates will not affect each other. The proposed method reduces the errors of the model weights. From computer simulation results, it is seen that the proposed method performs better than the general weighted method on moving target tracking.
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Chen, Zong-Kui, and 陳宗奎. "Robust and fast Kalman algorithms with application in target tracking." Thesis, 1987. http://ndltd.ncl.edu.tw/handle/01368398595196717554.
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Nguyen, Trang M. "Comparison of sampling based algorithms for multisensor distributed target tracking." 2003. http://louisdl.louislibraries.org/u?/NOD,37.
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Thesis (M.S.)--University of New Orleans, 2003.Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science"--Thesis t.p. Vita. Includes bibliographical references.
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"EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606406/index.pdf.
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de, la Parra Francisco. "A Sensor Network Querying Framework for Target Tracking." Thesis, 2009. http://hdl.handle.net/1974/1713.
Full textAbstract:
Successful tracking of a mobile target with a sensor network requires effective answers to the challenges of uncertainty in the measured data, small latency in acquiring and reporting the tracking information, and compliance with the stringent constraints imposed by the scarce resources available on each sensor node: limited available power, restricted availability of the inter-node communication links, relatively moderate computational power.
This thesis introduces the architecture of a hierarchical, self-organizing, two-tier, mission-specific sensor network, composed of sensors and routers, to track the trajectory and velocity of a single mobile target in a two-dimensional convex sensor field. A query-driven approach is proposed to input configuration parameters to the network, which allow sensors to self-configure into regions, and routers into tree-like structures, with the common goal of sensing and tracking the target in an energy-aware manner, and communicating this tracking data to a base station node incurring low-overhead responses, respectively.
The proposed algorithms to define and organize the sensor regions, establish the data routing scheme, and create the data stream representing the real-time location/velocity of a target, are heuristic, distributed, and represent localized node collaborations. Node behaviours have been modeled using state diagrams and inter-node collaborations have been designed using straightforward messaging schemes.
This work has attempted to establish that by using a query-driven approach to track a target, high-level knowledge can be injected to the sensor network self-organization processes and its following operation, which allows the implementation of an energy-efficient, low-overhead tracking scheme. The resulting system, although built upon simple components and interactions, is complex in extension, and not directly available for exact evaluation. However, it provides intuitively advantageous behaviours.Thesis (Master, Computing) -- Queen's University, 2009-03-04 11:18:14.392
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Chang, Wei-Yi, and 張偉毅. "A study of multiple target tracking and threat assessment algorithms for aerial objects." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/83522819316873631179.
Full textAbstract:
碩士國立中正大學
資訊工程研究所
103
Tracking algorithm can be used for pedestrian detection, gesture recognition and aerial object detection…etc. A lot of applications have been found. Our research focuses on multiple target tracking (MTT) algorithm particularly for aerial object detection. We studied three basic MTT：joint probability data association (JPDA), Interacting Multiply Models(IMM) and multiple hypothesis tracker (MHT). Each of them has constraints and will generate errors in real situation. We improved the performance of these algorithms. For example, we combine IMM with JPDA as a new method that has advantages from both sides. We also used clustering and Murty algorithm to greatly reduce the “combination explosion” of joint events. For MHT, we use track-oriented MHT (TOMHT) and greedy randomized adaptive search procedure (GRASP) for optimization MHT. Since the purpose of our research is aerial object tracking, it is difficult to test with real data. We overcome it by establishing a simulation system, which can randomly generate noise and different targets with different kinds of moving pattern. Finally, we established the method to analyze the characteristics of MTT algorithms, which is another key of our study.
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Huang, Bo-Shiun, and 黃柏勛. "Monocular Vision Single Image Based Motion Control for Autonomous Mobile Robot Target Tracking." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/35833843848653117237.
Full textAbstract:
碩士中原大學
機械工程研究所
100
Due to the rapid improvement of the autonomous mobile robot technology in recent years, autonomous mobile robots have been widely applied to a variety of domains such as medical operations, healthcare, and security. The development of visual tracking systems plays a key role in expanding and enhancing the functions and applications of autonomous mobile robots. An optimal, or at least suitable, visual tracking system should possess high accuracy and use few resources in hardware and software. This thesis proposes a new motion control method, based on monocular vision and single image, for autonomous mobile robot target tracking. The proposed method predicts a moving target’s position in an image through a particle filter. Due to the stochastic properties of particle filtering, the proposed method can effectively and accurately handle both linear and nonlinear dynamic motions. In addition, the proposed method uses simple polynomial calculations to map a target’s virtual position to its real-world coordinates. Thus, the proposed method needs few software resources for computation. Moreover, the proposed method adopts the monocular vision approach, i.e., it uses a single camera, and therefore it needs few hardware resources for implementation. The proposed method predicts a moving target’s position in an image, and calculates the virtual position’s real-world coordinates relative to a mobile robot. Based on to the target’s relative coordinates, the mobile robot is commanded to move towards the target in order to keep the target at the camera’s central field of view. Experimental results show that the proposed method can produce acceptable to good results in linear and nonlinear tracking experiments, and has an overall better tracking performance than the Kalman filter approach.
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Tien-WenSung and 宋天文. "Distributed Voronoi-based Coverage Enhancement and Target Tracking Handover Algorithms in Visual Sensor Networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/52935672714642015497.
Full textAbstract:
博士國立成功大學
電腦與通信工程研究所
102
A visual sensor network (VSN) consists of directional visual sensors, typically camera sensors, instead of omnidirectional or scalar sensors in wireless sensor networks (WSNs). The conditions of a VSN are dissimilar from those of an omnidirectional WSN, especially on the sensing coverage. The sensing coverage of a VSN depends on not merely the locations but also the directionality and sensing angle of the deployed sensors. In addition, the achievement of target tracking by a VSN highly depends on the visual coverage and acquired image clarity of the visual sensors. This dissertation aims at the coverage and target tracking issues and proposed Voronoi Diagram-based solutions for the surveillance application of wide-area large-scale VSNs. A Voronoi Diagram can divide a given region into sub-regions, thus Voronoi cells. Each cell is associated with only one sensor and any point in the cell has a shorter distance from the associated sensor than those from the other sensors. There is only one related work of using Voronoi Diagram to enhance the coverage of a directional sensor networks (DSN). With regard to the VSN target tracking, Voronoi Diagram has not utilized in related works. In brief, Voronoi Diagram has not drawn much attention in the VSNs. Hence this dissertation utilizes the concept and characteristic of a Voronoi Diagram and proposes coverage improvement algorithms and target tracking handover protocol for VSNs to perform a better performance in the criteria of working coverage, target-detected latency, target-tracked ratio and target tracking distance. The proposed scheme constructs local Voronoi cells by using a distributed method which is different from the conventional centralized construction of a Voronoi Diagram. Hence the algorithms proposed in this dissertation are all distributed and need no global information. The distributed Voronoi cell construction also can bring the benefits of fault tolerance and graceful degradation to adapt the proposed algorithms to the occurrence of sensor malfunction. Four major works were completed based on the construction of distributed Voronoi cells. The first is four basic algorithms using Voronoi Diagram and Delaunay Triangulation; and to compare and explore their coverage performances. The second is to propose an advanced Voronoi-based improvement algorithm for VSN sensing coverage. The third aims at a mobile VSN consists of mobile visual sensors to propose a coverage improvement approach. The final is a Voronoi-based handover protocol for target tracking in a VSN. The proposed schemes in this dissertation were also evaluated and compared with other different methods and the performance results show that they have good improvements. The contribution of this dissertation is to provide a well-performed and distributed approach on sensing coverage and target tracking topics for the wide-area surveillance applications of large-scale VSNs.
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Su, Feng. "Computer vision in target pursuit using a UAV." Thesis, 2018. http://hdl.handle.net/1959.7/uws:50228.
Full textAbstract:
Research in target pursuit using Unmanned Aerial Vehicle (UAV) has gained attention in recent years, this is primarily due to decrease in cost and increase in demand of small UAVs in many sectors. In computer vision, target pursuit is a complex problem as it involves the solving of many sub-problems which are typically concerned with the detection, tracking and following of the object of interest. At present, the majority of related existing methods are developed using computer simulation with the assumption of ideal environmental factors, while the remaining few practical methods are mainly developed to track and follow simple objects that contain monochromatic colours with very little texture variances. Current research in this topic is lacking of practical vision based approaches. Thus the aim of this research is to fill the gap by developing a real-time algorithm capable of following a person continuously given only a photo input.
As this research considers the whole procedure as an autonomous system, therefore the drone is activated automatically upon receiving a photo of a person through Wi-Fi. This means that the whole system can be triggered by simply emailing a single photo from any device anywhere. This is done by first implementing image fetching to automatically connect to WIFI, download the image and decode it. Then, human detection is performed to extract the template from the upper body of the person, the intended target is acquired using both human detection and template matching. Finally, target pursuit is achieved by tracking the template continuously while sending the motion commands to the drone.
In the target pursuit system, the detection is mainly accomplished using a proposed human detection method that is capable of detecting, extracting and segmenting the human body figure robustly from the background without prior training. This involves detecting face, head and shoulder separately, mainly using gradient maps. While the tracking is mainly accomplished using a proposed generic and non-learning template matching method, this involves combining intensity template matching with colour histogram model and employing a three-tier system for template management. A flight controller is also developed, it supports three types of controls: keyboard, mouse and text messages. Furthermore, the drone is programmed with three different modes: standby, sentry and search.
To improve the detection and tracking of colour objects, this research has also proposed several colour related methods. One of them is a colour model for colour detection which consists of three colour components: hue, purity and brightness. Hue represents the colour angle, purity represents the colourfulness and brightness represents intensity. It can be represented in three different geometric shapes: sphere, hemisphere and cylinder, each of these shapes also contains two variations.
Experimental results have shown that the target pursuit algorithm is capable of identifying and following the target person robustly given only a photo input. This can be evidenced by the live tracking and mapping of the intended targets with different clothing in both indoor and outdoor environments. Additionally, the various methods developed in this research could enhance the performance of practical vision based applications especially in detecting and tracking of objects.
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Neon, S. "Standoff Target Tracking Guidance using Line-of-Sight Distance Bifurcation." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5933.
Full textAbstract:
Unmanned aerial vehicle (UAV) applications that continue to receive significant attention are target acquisition and tracking. There exist many paths to track a stationary target from a prescribed altitude. The most widely used ones are straight lines and circular orbits. Circling a target at a constant radial distance is known as standoff tracking or circumnavigation. In this regard, an evolved requirement by the UAV is to reach a specified radial distance from the target within the stipulated time and continue circumnavigating the target with that radial distance. In doing so, it is desired that the UAV uses an easily computable guidance command with deterministic performance characteristics.
The thesis addresses the standoff target tracking problem by considering a modified two-parameter transcritical bifurcation in UAV-target line-of-sight distance dynamics. Appropriate choice of the bifurcation parameters results in the existence of a stable equilibrium point of the proposed line-of-sight distance dynamics which corresponds to the desired standoff radius. Further analysis relates the control parameters to the desired settling time, that is, the time taken by the UAV to settle on the desired standoff circle. A closed-form analytical expression is derived for the set of achievable settling times as a function of the two bifurcation parameters, the UAV speed, and initial separation. Simulation studies are carried out by considering a second-order heading-hold autopilot, a first-order speed control, and a limited turn rate for the UAV. Additional simulation studies are performed for realistic scenarios considering the presence of wind, noisy sensor measurements, and variable initial conditions. Simulation results demonstrate the robustness of the proposed guidance algorithm in achieving standoff target tracking with a constraint on the settling time. Overall, the proposed method offers a simple and easy-to-implement guidance solution.
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Chen, Jiun-Fu, and 陳俊甫. "Extended Machine Perception in Multi-Target Tracking with Occlusion: from Single Sensor to Heterogeneous Sensors." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/53v72k.
Full textAbstract:
博士國立臺灣大學
資訊工程學研究所
107
Multi-Target tracking is a key ability for many intelligent systems in lots of applications. In order to accomplish the multi-target tracking, the measurements from the perceptive sensor plays a very important role. It is impossible to perform the multi-target tracking without sensory data especially such as occlusion situation, which increases the difficulty of the tracking task. Moreover, in the urban traffic situation, occlusion decreases the driving safety; and in the case of human joint tracking, occlusion may fails the estimates and leads to wrong judgement for evaluating the performance of rehabilitation activities. Here, two frameworks are presented and described for a stationary 2D LIDAR and for heterogeneous sensors. The first framework introduces the virtual measurement model with interacting object tracking scheme to tackle the effects of the occlusion in crowded urban environments. The second framework applies the heterogeneous sensor simultaneous localization, tracking, and modeling algorithm to fuse heterogeneous sensors and to provide estimates within occlusion for motion evaluation in stroke rehabilitation process. The ample experimental results of the first application show that the interact object tracking scheme tracks over 57% of occluded moving object for the daunting task in an urban intersection. While the results of the second application with synthetic data and collected from ten subjects reveal that the proposed approach yields 4.6 cm error in observed cases and 18.1 cm error during burst occlusion. We successfully demonstrate the capability to resolve issues and effects in occlusion for both urban and indoor environments.