Journal articles on the topic 'Multiple target tracking algorithms'
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1
Wu, Jin, Changqing Cao, Yuedong Zhou, Xiaodong Zeng, Zhejun Feng, Qifan Wu, and Ziqiang Huang. "Multiple Ship Tracking in Remote Sensing Images Using Deep Learning." Remote Sensing 13, no. 18 (September 9, 2021): 3601. http://dx.doi.org/10.3390/rs13183601.
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In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study. The feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization ability of similar images. We compared the proposed algorithm with other state-of-the-art multi-object tracking algorithms. Results show that the tracking accuracy is improved by 2.23%, while the average running speed is close to 21 frames per second, meeting the needs of real-time tracking.
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Ding, Ma. "Tracking Target Identification Model Based on Multiple Algorithms." Applied Mechanics and Materials 539 (July 2014): 106–12. http://dx.doi.org/10.4028/www.scientific.net/amm.539.106.
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In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.
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Ling, Jiankun. "Target Tracking Using Kalman Filter Based Algorithms." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012020. http://dx.doi.org/10.1088/1742-6596/2078/1/012020.
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Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
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Hoang, Le Minh, Aleksandr A. Konovalov, and Dao Van Luc. "Tracking of Maneuvering Targets Using a Variable Structure Multiple Model Algorithm." Journal of the Russian Universities. Radioelectronics 26, no. 3 (July 6, 2023): 77–89. http://dx.doi.org/10.32603/1993-8985-2023-26-3-77-89.
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Introduction. In recent years, much attention has been paid to the development of trajectory filtering methods for tracking maneuvering targets. Multi-model (MM) algorithms are widely used for filtering maneuvering targets. Conventional MM algorithms are characterized by a fixed structure. However, highly maneuvering targets require a sufficiently large set of models covering the entire range of possible maneuvers, although an increase in the number of models cannot ensure an increase in the accuracy of tracking. To overcome these problems, multiple model algorithms with a variable structure (VSMM) were proposed. This article proposes two VSMM algorithms for tracking maritime targets performing a coordinated turn at constant speed. These are algorithms with a variable set of models based on adaptive grid and switching grid methods.Aim. To develop an adaptive trajectory tracking algorithm that uses a constant turn model to track maneuvering surface objects.Materials and methods. The resulting algorithm is based on the theory of grid adaptation in multi-model estimation methods and is used to estimate the components of the coordinate and velocity vectors of surface maneuvering targets. The algorithm efficiency was evaluated using computer statistical modeling in the MATLAB environment.Results. The structure of an adaptive VSMM algorithm was described. Simulations were carried out to confirm the algorithm efficiency. In the considered simulation scenarios, the algorithm produces effective estimates of the coordinate vectors and speed of surface maneuvering targets.Conclusion. Adaptive algorithms improve the efficiency of target tracking in comparison with multi-model algorithms with a fixed structure, at the same time as saving computational resources.
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Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.
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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.
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Lei Shundong. "Tracking Target Identification Model Based on Multiple Algorithms." International Journal of Digital Content Technology and its Applications 7, no. 3 (February 15, 2013): 274–83. http://dx.doi.org/10.4156/jdcta.vol7.issue3.35.
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Memon, Sufyan, Myungun Kim, and Hungsun Son. "Tracking and Estimation of Multiple Cross-Over Targets in Clutter." Sensors 19, no. 3 (February 12, 2019): 741. http://dx.doi.org/10.3390/s19030741.
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Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward track in the current time. This novel idea is utilized in the joint integrated track splitting (JITS) filter to develop a new fixed-interval smoothing JITS (FIsJITS) algorithm for tracking multiple cross-over targets. The FIsJITS initializes tracks employing JITS in two-way directions: Forward-time moving JITS (fJITS) and backward-time moving JITS (bJITS). The fJITS acquires the bJITS predictions when they arrive from future scans to the current scan for smoothing. As a result, the smoothing multi-target data association probabilities are obtained for computing the fJITS and smoothing output estimates. This significantly improves estimation accuracy for multiple cross-over targets in heavy clutter. To verify this, numerical assessments of the FIsJITS are tested and compared with existing algorithms using simulations.
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Memon, Sufyan Ali, Hungsun Son, Wan-Gu Kim, Abdul Manan Khan, Mohsin Shahzad, and Uzair Khan. "Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace." Drones 7, no. 4 (March 30, 2023): 241. http://dx.doi.org/10.3390/drones7040241.
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In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm.
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Chen, Yuntao, Bin Wu, guangzhi Luo, xiaoyan Chen, and junlin Liu. "Multi-target tracking algorithm based on YOLO+DeepSORT." Journal of Physics: Conference Series 2414, no. 1 (December 1, 2022): 012018. http://dx.doi.org/10.1088/1742-6596/2414/1/012018.
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Abstract After several years of development, the multi-target tracking algorithm has significantly transitioned from being researched to being put into practical production and life. The application field of human detection and tracking technology is closely related to our daily life. In order to solve the problems of the background complexity, the diversity of object shapes in the application of multi-target algorithms, and the mutual occlusion between multiple tracking targets and the lost target, this paper improves the DeepSORT target tracking algorithm, uses the improved YOLO network to detect pedestrians, inputs the detection frame to the Kalman filter for prediction output, and then uses the Hungarian algorithm to realize a tracking frame and detection frame of the predicted output. The experimental results show that target tracking accuracy is increased by 4.3%, the running time is the shortest, and the number of successfully tracked targets is relatively high.
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Song, Xiyu, Nae Zheng, and Ting Bai. "Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems." International Journal of Antennas and Propagation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7241281.
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Considering the demands of different location accuracy for multiple targets tracking, performance-driven resource allocation schemes in distributed MIMO radar system are proposed. Restricted by the tracking antenna number, location estimation mean-square error (MSE), and target priorities, an optimization problem of the minimal antenna subsets selection is modeled as a knapsack problem. Then, two operational schemes, modified fair multistart local search (MFMLS) algorithm and modified fair multistart local search with one antenna to all targets (MFMLS_OAT) algorithm, are presented and evaluated. Simulation results indicate that the proposed MFMLS and MFMLS_OAT algorithm outperform the existing algorithms. Moreover, the MFMLS algorithm can distinguish targets with different priorities, while the MFMLS_OAT algorithm can perform the tracking tasks with higher accuracy.
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Jovandic, Igor, Zeljko Djurovic, and Branko Kovacevic. "Adaptive filtering algorithms in target tracking applications." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 317–26. http://dx.doi.org/10.2298/fuee0303317j.
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Comparison of several target tracking algorithms is presented. Namely discrete noise level adjustment (DNLA), variable state dimension (VSD) and interacting multiple model (IMM) algorithms are discussed. Target trajectory, target models, filtering algorithms and simulation results are given. The cumulative estimation error criterion is used in order to compare the algorithms.
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Lang, Hong, and Cong Shan. "Bias phenomenon and compensation in multiple target tracking algorithms." Mathematical and Computer Modelling 31, no. 8-9 (April 2000): 147–65. http://dx.doi.org/10.1016/s0895-7177(00)00063-7.
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Sheng, Xueli, Yang Chen, Longxiang Guo, Jingwei Yin, and Xiao Han. "Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks." Sensors 18, no. 10 (September 21, 2018): 3193. http://dx.doi.org/10.3390/s18103193.
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Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.
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Li, Zhen-Xing, Yun Wang, Jin-Mang Liu, Ni Peng, and Lin-Hai Gan. "Variable-structure interacting multiple-model estimation for group targets tracking with random matrices." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 7 (February 9, 2017): 1201–11. http://dx.doi.org/10.1177/0954410016688123.
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In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of different structures to constitute a candidate model set and selects a minimum difference model from target state as the present extended model from the set of candidates at real time. Second, in the filtering process of VSIMM, the fusion estimation of extension state is implemented by the scalar coefficients weighting method, where weight coefficient is calculated by the trace of the corresponding covariance matrix of extension state. The performances of the proposed EMA-VSIMM and BMA-VSIMM algorithms are evaluated via simulation of a generic group targets maneuvering tracking problem.
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Song, Shiping, Jian Wu, Sumin Zhang, Yunhang Liu, and Shun Yang. "Research on Target Tracking Algorithm Using Millimeter-Wave Radar on Curved Road." Mathematical Problems in Engineering 2020 (June 27, 2020): 1–21. http://dx.doi.org/10.1155/2020/3749759.
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Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.
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Liu, Qiaoran, and Xun Yang. "Improved Interacting Multiple Model Particle Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (February 2018): 169–75. http://dx.doi.org/10.1051/jnwpu/20183610169.
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For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.
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Zhang, Zheng, Cong Huang, Fei Zhong, Bote Qi, and Binghong Gao. "Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision." Complexity 2021 (May 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/5526831.
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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.
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Yang, Haiyan, Hongqiang Liu, Zhongliang Zhou, and An Xu. "A practical adaptive nonlinear tracking algorithm with range rate measurement." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771877686. http://dx.doi.org/10.1177/1550147718776863.
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It is difficult to answer the problem whether the range rate measurement should be adopted to track a target in a tracking scenario. A practical adaptive nonlinear tracking algorithm with the range rate measurement is proposed, which avoids this problem and achieves good accuracy of target state estimation. First, three popular nonlinear filtering algorithms only with the position measurement are surveyed. Second, three popular nonlinear filtering algorithms with the position and range rate measurements are surveyed. Then, a novel tracking algorithm with range rate measurement is proposed based on the cumulative sum detector and the above two kinds of nonlinear algorithms. The results of simulation experiment demonstrate that the range rate measurement could reduce accuracy of the target state estimation in mismatch tracking scenarios. The results of simulation experiment also verify that the performance of proposed algorithm is better than the current state and the art interacting multiple-model algorithm and can well follow the state estimation output of the measurement equation matching the tracking scenario.
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Yang, Tao, Dongdong Li, Yi Bai, Fangbing Zhang, Sen Li, Miao Wang, Zhuoyue Zhang, and Jing Li. "Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos." Remote Sensing 11, no. 19 (September 29, 2019): 2278. http://dx.doi.org/10.3390/rs11192278.
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In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.
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Wang, Quanhui, En Fan, and Pengfei Li. "Fuzzy-Logic-Based, Obstacle Information-Aided Multiple-Model Target Tracking." Information 10, no. 2 (February 2, 2019): 48. http://dx.doi.org/10.3390/info10020048.
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Incorporating obstacle information into maneuvering target-tracking algorithms may lead to a better performance when the target when the target maneuver is caused by avoiding collision with obstacles. In this paper, we propose a fuzzy-logic-based method incorporating new obstacle information into the interacting multiple-model (IMM) algorithm (FOIA-MM). We use convex polygons to describe the obstacles and then extract the distance from and the field angle of these obstacle convex polygons to the predicted target position as obstacle information. This information is fed to two fuzzy logic inference systems; one system outputs the model weights to their probabilities, the other yields the expected sojourn time of the models for the transition probability matrix assignment. Finally, simulation experiments and an Unmanned Aerial Vehicle experiment are carried out to demonstrate the efficiency and effectiveness of the proposed algorithm.
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Cichella, Venanzio, and Isaac Kaminer. "Coordinated Vision-Based Tracking by Multiple Unmanned Vehicles." Drones 7, no. 3 (March 5, 2023): 177. http://dx.doi.org/10.3390/drones7030177.
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We address the problem of coordinated vision-based tracking of a moving target using multiple unmanned vehicles that exchange information over a supporting time-varying network. The objective of this work is to formulate decentralized control algorithms that enable multiple vehicles to follow the target while coordinating their phase separation. A typical scenario involves multiple unmanned aerial vehicles that are required to monitor a moving ground object (target tracking) while maintaining a desired inter-vehicle separation (coordination). To solve the vision-based tracking problem, the yaw rate of each vehicle is used as the control input, while the speeds of the vehicles are adjusted to achieve coordination. It is assumed that the vehicles are equipped with an internal autopilot, which is able to track yaw rate and speed commands. The performance of the coordinated vision-based tracking algorithm is evaluated as a function of the target’s velocity, tracking performance of the onboard autopilot, and the quality of service of the communication network.
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Sun, Lifan, Haofang Yu, Zhumu Fu, Zishu He, and Fazhan Tao. "Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm." Applied Sciences 10, no. 14 (July 21, 2020): 5004. http://dx.doi.org/10.3390/app10145004.
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For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.
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Dai, Qiuyang, and Faxing Lu. "A New Spatial Registration Algorithm of Aerial Moving Platform to Sea Target Tracking." Sensors 23, no. 13 (July 3, 2023): 6112. http://dx.doi.org/10.3390/s23136112.
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Spatial registration is the primary challenge affecting target tracking accuracy, especially for the aerial moving platform and sea target tracking. In this environment, it is important to account for both the errors in sensor observations and the variations in platform attitude. In order to solve the problem of complex types of errors in the tracking of sea targets by aerial moving platforms, a new spatial registration algorithm is proposed. Through separating and analyzing observation data, the influence of sensor observation error and attitude error on observation data is obtained, and a systematic error consistency matrix is established. Based on observation information from multiple platforms, accurate tracking of sea targets can be accomplished without estimating systematic error. In order to verify the effectiveness of the algorithm, we carried out simulation experiments and practical experiments on the lake, which showed that the new algorithm was more efficient than traditional algorithms.
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Hong, Xiaobin, Bin Cui, Weiguo Chen, Yinhui Rao, and Yuanming Chen. "Research on Multi-Ship Target Detection and Tracking Method Based on Camera in Complex Scenes." Journal of Marine Science and Engineering 10, no. 7 (July 17, 2022): 978. http://dx.doi.org/10.3390/jmse10070978.
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Aiming at the problem that multi-ship target detection and tracking based on cameras is difficult to meet the accuracy and speed requirements at the same time in some complex scenes, an improved YOLOv4 algorithm is proposed, which simplified the network of the feature extraction layer to obtain more shallow feature information and avoid the disappearance of small ship target features, and uses the residual network to replace the continuous convolution operation to solve the problems of network degradation and gradient disappearance. In addition, a nonlinear target tracking model based on the UKF method is constructed to solve the problem of low real-time performance and low precision in multi-ship target tracking. Multi-ship target detection and tracking experiments were carried out in many scenes with large differences in ship sizes, strong background interference, tilted images, backlight, insufficient illumination, and rain. Experimental results show that the average precision of the detection algorithm of this paper is 0.945, and the processing speed is about 34.5 frame per second, where the real-time performance is much better than other algorithms while maintaining high precision. Furthermore, the multiple object tracking accuracy (MOTA) and the multiple object tracking precision (MOTP) of this paper algorithm are 76.4 and 80.6, respectively, which are both better than other algorithms. The method proposed in this paper can realize the ship target detection and tracking well, with less missing detection and false detection, and also has good accuracy and real-time performance. The experimental results provide a valuable theoretical reference for the further practical application of the method.
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Hussein, Zeinab, and Omar Banimelhem. "Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors." Journal of Sensor and Actuator Networks 12, no. 2 (April 13, 2023): 35. http://dx.doi.org/10.3390/jsan12020035.
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Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction.
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Zhou, Fansen, Yidi Wang, Wei Zheng, Zhao Li, and Xin Wen. "Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target." Remote Sensing 14, no. 17 (August 28, 2022): 4239. http://dx.doi.org/10.3390/rs14174239.
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The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.
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Lu, Jiaxin, Feifeng Liu, Hongjie Liu, and Quanhua Liu. "Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas." Remote Sensing 14, no. 4 (February 14, 2022): 902. http://dx.doi.org/10.3390/rs14040902.
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Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.
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Kou, Kunhu, Bochen Li, Lu Ding, and Lei Song. "A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter." Journal of Marine Science and Engineering 11, no. 4 (April 19, 2023): 858. http://dx.doi.org/10.3390/jmse11040858.
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Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories.
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Wang, Yi, Xin Chen, Chao Gong, and Peng Rao. "Non-Ellipsoidal Infrared Group/Extended Target Tracking Based on Poisson Multi-Bernoulli Mixture Filter and B-Spline." Remote Sensing 15, no. 3 (January 19, 2023): 606. http://dx.doi.org/10.3390/rs15030606.
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This study provides a solution for multiple group/extended target tracking with an arbitrary shape. Many tracking approaches for extended/group targets have been proposed. However, these approaches make assumptions about the target shape, which have limitations in practical applications. To address this problem, in this work, an extended/group target tracking algorithm based on B-spline is proposed. Specifically, the extension of an extended or a group target was modeled as a spatial probability distribution characterized by the control points of a B-spline function that was then jointly propagated with the measurement rate model and kinematic component model over time using the Poisson multi-Bernoulli mixture (PMBM) filter framework. In addition, an amplitude-aided measurement partitioning approach is proposed to improve the accuracy caused by distance-based approaches. The simulation results demonstrate that the extension, shape and orientation of targets can be estimated better by the proposed algorithm, even if the shape changes. The tracking performance is also improved by about 10% and 13% compared to the other two algorithms.
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Memon, Sufyan Ali, Min-Seuk Park, Imran Memon, Wan-Gu Kim, Sajid Khan, and Yifang Shi. "Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter." Sensors 22, no. 13 (June 23, 2022): 4759. http://dx.doi.org/10.3390/s22134759.
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This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
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Liu, Hong Jiang. "Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm." Applied Mechanics and Materials 190-191 (July 2012): 906–10. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.906.
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In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.
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Lo, K. W., and C. K. Li. "An improved multiple target angle tracking algorithm." IEEE Transactions on Aerospace and Electronic Systems 28, no. 3 (July 1992): 797–805. http://dx.doi.org/10.1109/7.256300.
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Sheng, Mingwei, Songqi Tang, Hongde Qin, and Lei Wan. "Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs." Sensors 19, no. 2 (January 17, 2019): 370. http://dx.doi.org/10.3390/s19020370.
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Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.
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Li, Changrui, and Qiuping Peng. "Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance." Security and Communication Networks 2022 (August 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/6579066.
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In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.
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Hong, Yong, Deren Li, Shupei Luo, Xin Chen, Yi Yang, and Mi Wang. "An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention." Remote Sensing 14, no. 24 (December 15, 2022): 6354. http://dx.doi.org/10.3390/rs14246354.
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Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction backbone network was combined with a self-built raster semantic map which was stored in the encoder for correlation and generated target position encoding and multi-dimensional feature vectors. The decoder incorporated four methods: spatial clustering and semantic filtering of multi-view targets; dynamic matching of multi-dimensional features; space–time logic-based multi-target tracking, and space–time convergence network (STCN)-based parameter passing. Through the fusion of multiple decoding methods, multi-camera targets were tracked in three dimensions: temporal logic, spatial logic, and feature matching. For the MOT17 dataset, this study’s method significantly outperformed the current state-of-the-art method by 2.2% on the multiple object tracking accuracy (MOTA) metric. Furthermore, this study proposed a retrospective mechanism for the first time and adopted a reverse-order processing method to optimize the historical mislabeled targets for improving the identification F1-score (IDF1). For the self-built dataset OVIT-MOT01, the IDF1 improved from 0.948 to 0.967, and the multi-camera tracking accuracy (MCTA) improved from 0.878 to 0.909, which significantly improved the continuous tracking accuracy and reliability.
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Huang, Yuan, Yifang Shi, and Taek Song. "An Efficient Multi-Path Multitarget Tracking Algorithm for Over-The-Horizon Radar." Sensors 19, no. 6 (March 20, 2019): 1384. http://dx.doi.org/10.3390/s19061384.
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In target tracking environments using over-the-horizon radar (OTHR), one target may generate multiple detections through different signal propagation paths. Trackers need to jointly handle the uncertainties stemming from both measurement origin and measurement path. Traditional multitarget tracking algorithms suffer from high computational loads in such environments since they need to enumerate all possible joint measurement-to-track assignments considering the measurements paths unless they employ some approximations regarding the measurements and their corresponding paths. In this paper, we propose a novel algorithm, named multi-path linear multitarget integrated probabilistic data association (MP-LM-IPDA), to efficiently track multitarget in multiple detection environments. Instead of generating all possible joint assignments, MP-LM-IPDA calculates the modulated clutter measurement density for each measurement cell of each track. The modulated clutter measurement density considers the possibility that the measurement cells originate from the clutter as well as from other potential targets. By incorporating the modulated clutter measurement density, the single target tracking structure can be applied for multitarget tracking, which significantly reduces the computational load. The simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.
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Chen, Zhi, Peizhong Liu, Yongzhao Du, Yanmin Luo, and Wancheng Zhang. "Correlation Tracking via Self-Adaptive Fusion of Multiple Features." Information 9, no. 10 (September 27, 2018): 241. http://dx.doi.org/10.3390/info9100241.
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Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.
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Tang, Xinmin, Wenjie Zhao, and Shangfeng Gao. "Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors." International Journal of Distributed Sensor Networks 16, no. 2 (February 2020): 155014772090456. http://dx.doi.org/10.1177/1550147720904563.
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To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced.
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Liu, Yu, and Xiaoyan Wang. "Mean Shift Fusion Color Histogram Algorithm for Nonrigid Complex Target Tracking in Sports Video." Complexity 2021 (April 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/5569637.
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We analyze and study the tracking of nonrigid complex targets of sports video based on mean shift fusion color histogram algorithm. A simple and controllable 3D template generation method based on monocular video sequences is constructed, which is used as a preprocessing stage of dynamic target 3D reconstruction algorithm to achieve the construction of templates for a variety of complex objects, such as human faces and human hands, broadening the use of the reconstruction method. This stage requires video sequences of rigid moving target objects or sets of target images taken from different angles as input. First, the standard rigid body method of Visuals is used to obtain the external camera parameters of the sequence frames as well as the sparse feature point reconstruction data, and the algorithm has high accuracy and robustness. Then, a dense depth map is computed for each input image frame by the Multi-View Stereo algorithm. The depth reconstruction with a too high resolution not only increases the processing time significantly but also generates more noise, so the resolution of the depth map is controlled by parameters. The multiple hypothesis target tracking algorithms are used to track multiple targets, while the chunking feature is used to solve the problem of mutual occlusion and adhesion between targets. After finishing the matching, the target and background models are updated online separately to ensure the validity of the target and background models. Our results of nonrigid complex target tracking by mean shift fusion color histogram algorithm for sports video improve the accuracy by about 8% compared to other studies. The proposed tracking method based on the mean shift algorithm and color histogram algorithm can not only estimate the position of the target effectively but also depict the shape of the target well, which solves the problem that the nonrigid targets in sports video have complicated shapes and are not easy to track. An example is given to demonstrate the effectiveness and adaptiveness of the applied method.
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Lei, Yang, Yuan Wu, and Ahmad Jalal Khan Chowdhury. "Multi-target tracking algorithm in intelligent transportation based on wireless sensor network." Open Physics 16, no. 1 (December 31, 2018): 1000–1008. http://dx.doi.org/10.1515/phys-2018-0121.
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Abstract The traditional extended Kalman algorithm for multi-target tracking in the field of intelligent transportation does not consider the occlusion problem of the multi-target tracking process, and has the disadvantage of low multi-target tracking accuracy. A multi-target tracking algorithm using wireless sensors in an intelligent transportation system is proposed. Based on the dynamic clustering structure, the measurement results of each sensor are the superimposed results of sound signals and environmental noise from multiple targets. During the tracking process, each target corresponds to a particle filter. When the target spacing is relatively close to each other, each master node realizes distributed multi-target tracking through information exchange. At the same time, it is also necessary to consider the overlap between adjacent frames. Since the moving target speed is too fast, the target occlusion has the least influence on the tracking accuracy, and can accurately track multiple targets. The experimental results show that the proposed algorithm has a target tracking error of 0.5 m to 1 m, and the tracking result has high precision.
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Duan, Shaolou, Lingfeng Meng, Delong Ma, and Liangyu Mi. "Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream." Scientific Programming 2021 (December 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/2702272.
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With the continuous progress of science and technology, the sport of roller skating has developed rapidly and the technical level of the game has become higher and higher. Its sport performance has been rapidly improved. However, China’s roller skating is relatively late, and there is still a certain gap compared with many Western developed countries. In order to improve the performance of China’s roller skating, this study takes the representative Chinese and foreign excellent speed skaters as the research object and compares the sprinting technology of Chinese and foreign excellent speed skaters by using image measurement and image analysis to obtain the kinematic parameters and data of the athletes’ sprinting technology in the competition state. In view of the problem that the current video target tracking algorithm is easy to follow multiple targets, a video multiobject detection and tracking algorithm with improved tracking learning detection (TLD) is studied with the skater in the video as the research object. For the lost target, the prediction function of Kalman filter algorithm is used to track the trajectory of the typical target in the video, and the trajectory tracked by Kalman filter algorithm is used to compensate the lost part of TLD algorithm, so as to obtain the complete trajectory of the typical target in the video to improve the accuracy of video multiobject tracking. Since the existing trajectory prediction algorithms have the limitation of poor accuracy, a social-long short-term memory (Social-LSTM) network-based video typical target trajectory prediction algorithm is proposed to predict the trajectory sequences of typical targets to be detected by incorporating the contextual environment information and the interaction relationship between multiple target trajectories into the Social-LSTM network. The simulation results show that the proposed trajectory prediction algorithm outperforms the traditional LSTM algorithm, Hidden Markov Model Algorithm, and Hybrid Gaussian model algorithm, which is helpful to improve the accuracy of video roller skater target trajectory prediction, and the tracking success rate is 0.98.
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Liu, Ya Lei, and Xiao Hui Gu. "Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array." Applied Mechanics and Materials 300-301 (February 2013): 407–13. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.407.
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Abstract. In order to improve the tracking accuracy of 3D dynamic acoustic array to 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The 3D motion acoustic array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.
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Fu, X., Y. Jia, F. Yu, and J. Du. "New interacting multiple model algorithms for the tracking of the manoeuvring target." IET Control Theory & Applications 4, no. 10 (October 1, 2010): 2184–94. http://dx.doi.org/10.1049/iet-cta.2009.0583.
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Ali, Wasiq, Yaan Li, Muhammad Asif Zahoor Raja, and Nauman Ahmed. "Generalized pseudo Bayesian algorithms for tracking of multiple model underwater maneuvering target." Applied Acoustics 166 (September 2020): 107345. http://dx.doi.org/10.1016/j.apacoust.2020.107345.
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Hossain and Lee. "Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices." Sensors 19, no. 15 (July 31, 2019): 3371. http://dx.doi.org/10.3390/s19153371.
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In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.
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Zhang, Jianming, You Wu, Xiaokang Jin, Feng Li, and Jin Wang. "A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate." Mathematical Problems in Engineering 2018 (December 24, 2018): 1–14. http://dx.doi.org/10.1155/2018/5986062.
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Object tracking is a vital topic in computer vision. Although tracking algorithms have gained great development in recent years, its robustness and accuracy still need to be improved. In this paper, to overcome single feature with poor representation ability in a complex image sequence, we put forward a multifeature integration framework, including the gray features, Histogram of Gradient (HOG), color-naming (CN), and Illumination Invariant Features (IIF), which effectively improve the robustness of object tracking. In addition, we propose a model updating strategy and introduce a skewness to measure the confidence degree of tracking result. Unlike previous tracking algorithms, we judge the relationship of skewness values between two adjacent frames to decide the updating of target appearance model to use a dynamic learning rate. This way makes our tracker further improve the robustness of tracking and effectively prevents the target drifting caused by occlusion and deformation. Extensive experiments on large-scale benchmark containing 50 image sequences show that our tracker is better than most existing excellent trackers in tracking performance and can run at average speed over 43 fps.
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Shawky, Negm Eldin Mohamed. "Accuracy Enhancement of GPS for Tracking Multiple Drones Based on MCMC Particle Filter." International Journal of Security and Privacy in Pervasive Computing 12, no. 1 (January 2020): 1–16. http://dx.doi.org/10.4018/ijsppc.2020010101.
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GPS information when received from multi-unmanned aerial vehicles (UAVs), also called drones, via a ground control station can be processed for detecting and tracking estimate target position. Tracking drones based on GPS has had some issues with missed received information or received information with an error that can lead to lost tracking. The proposed algorithm, Markov chain Monte Carlo based particle filter (MCMC-PF) can be used to overcome these issues of error in received information with keeping tracks and provides continuous tracking with a higher accuracy. This is suitable for real time applications that deal with GPS receiver devices with low efficiency during tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithms of the Kalman filter (KF).
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Moiseev, A. A. "Posterior tracking of multiple target elements." Radio industry (Russia) 30, no. 2 (June 6, 2020): 25–31. http://dx.doi.org/10.21778/2413-9599-2020-30-2-25-31.
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The posterior method of selecting and tracking the elements (objects) of a group radar target is considered. It allows for estimating the number of elements in a group target and carrying out their preliminary selection. It also helps in evaluating the tracking characteristics of the selected elements. A feature of the method is the preliminary accumulation of a data array to be processed further out of real-time. An histograms analysis is constructed to achieve that based on the processing results of the specified array. The corresponding algorithm provides for variations construction in the spread of the initial moments of the pulses relative to the average value in the packet, as well as modes in the packet and strobe. The decisive function in this case is defined as the product of these variations, and the decisive rule is to find the value of the spacing of the pulse numbers at which the minimum value of the decisive function is achieved. This value is interpreted as the number of elements in the group. The possibility of splitting a group into separate elements using the proposed approach is demonstrated. Mathematical modeling of the tracking procedure allowed comparing two possible methods. The first one is the “strongest neighbor” method, which provides for the choice of the continuation of the pulse with the maximum amplitude in the strobe. The second one is the “nearest neighbor” method, which provides the choice of the continuation of the pulse with the median moment of registration in the strobe. A comparative analysis of these two approaches demonstrated the preference of the “nearest neighbor” method as the one to provide less distortion of the time dependences of the amplitude and frequency. Evaluation of tracking parameters carried out according to the constructed histograms made it also possible to assess the probability of detecting an object during tracking.
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Shen-Tu, Han, Hanming Qian, Dongliang Peng, Yunfei Guo, and Ji-An Luo. "An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm." Sensors 19, no. 2 (January 17, 2019): 366. http://dx.doi.org/10.3390/s19020366.
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In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.
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He, Yang Ming, Yan Qiu He, Guang Yao Xiong, and Ming Feng Zhu. "A Robust Algorithm for Multiple Moving Targets Tracking." Applied Mechanics and Materials 556-562 (May 2014): 2638–41. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2638.
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An algorithm for multiple moving targets tracking in computer vision is put forward in this paper. The association between the tracking chains and new targets detected is mainly discussed. There are four situations. Among them, the most complicated is that several tracking chains exist and several new targets are detected. The distance between new targets and old targets is used to determine whether they are the same targets. When the tracking chain is found broken, it is seen that the target has passed the monitoring area, and begins to count. In the paper, the program code is shown, too. In the end of the paper, real images on the spot are used to test the algorithm. Its good effect can be proved from practical spot.