Academic literature on the topic 'Unambiguous dynamic real time single object detection'

We propose a dynamic projection mapping system with effective machine-learning and high-speed edge-based object tracking using a single IR camera. The machine-learning techniques are used for precise 3D initial posture estimation from 2D IR images, as a detection process. After the detection, we apply an edge-based tracking process for real-time image projection. In this paper, we implement our proposal and actually achieve dynamic projection mapping. In addition, we evaluate the performance of our proposal through the comparison with a Kinect-based tracking system.

Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods for object detection and is even capable of exceeding two times the capabilities of other algorithms. You Only Look Once, an object detection method, is very fast because a single neural network predicts bounded box and class probabilities directly from the whole image in an evaluation. In this study, the object under study is an object that is around the researcher (a random thing). System design using Unified Modeling Language (UML) diagrams, including use case diagrams, activity diagrams, and class diagrams. This system will be built using the python language. Python is a high-level programming language that can execute some multi-use instructions directly (interpretively) with the Object Oriented Programming method and also uses dynamic semantics to provide a level of syntax readability. As a high-level programming language, python can be learned easily because it has been equipped with automatic memory management, where the user must run through the Anaconda prompt and then continue using Jupyter Notebook. The purpose of this study was to determine the accuracy and performance of detecting random objects on YOLOv3. The result of object detection will display the name and bounding box with the percentage of accuracy. In this study, the system is also able to recognize objects when they object is stationary or moving.

One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.

One key advantage of compressive sensing is that only a small amount of the raw video data is transmitted or saved. This is extremely important in bandwidth constrained applications. Moreover, in some scenarios, the local processing device may not have enough processing power to handle object detection and classification and hence the heavy duty processing tasks need to be done at a remote location. Conventional compressive sensing schemes require the compressed data to be reconstructed first before any subsequent processing can begin. This is not only time consuming but also may lose important information in the process. In this paper, we present a real-time framework for processing compressive measurements directly without any image reconstruction. A special type of compressive measurement known as pixel-wise coded exposure (PCE) is adopted in our framework. PCE condenses multiple frames into a single frame. Individual pixels can also have different exposure times to allow high dynamic ranges. A deep learning tool known as You Only Look Once (YOLO) has been used in our real-time system for object detection and classification. Extensive experiments showed that the proposed real-time framework is feasible and can achieve decent detection and classification performance.

Pedestrian detection has attracted great research attention in video surveillance, traffic statistics, and especially in autonomous driving. To date, almost all pedestrian detection solutions are derived from conventional framed-based image sensors with limited reaction speed and high data redundancy. Dynamic vision sensor (DVS), which is inspired by biological retinas, efficiently captures the visual information with sparse, asynchronous events rather than dense, synchronous frames. It can eliminate redundant data transmission and avoid motion blur or data leakage in high-speed imaging applications. However, it is usually impractical to directly apply the event streams to conventional object detection algorithms. For this issue, we first propose a novel event-to-frame conversion method by integrating the inherent characteristics of events more efficiently. Moreover, we design an improved feature extraction network that can reuse intermediate features to further reduce the computational effort. We evaluate the performance of our proposed method on a custom dataset containing multiple real-world pedestrian scenes. The results indicate that our proposed method raised its pedestrian detection accuracy by about 5.6–10.8%, and its detection speed is nearly 20% faster than previously reported methods. Furthermore, it can achieve a processing speed of about 26 FPS and an AP of 87.43% when implanted on a single CPU so that it fully meets the requirement of real-time detection.

More and more information on tourist spots is being represented as pictures rather than text. Consequently, tourists who are interested in a specific attraction shown in pictures may have no idea how to perform a text search to get more information about the interesting tourist spots. In the view of this problem and to enhance the competitiveness of the tourism market, this research proposes an innovative tourist spot identification mechanism, which is based on deep learning-based object detection technology, for real-time detection and identification of tourist spots by taking pictures on location or retrieving images from the Internet. This research establishes a tourist spot recognition system, which is a You Only Look Once version 3 model built in Tensorflow AI framework, and is used to identify tourist attractions by taking pictures with a smartphone's camera. To verify the possibility, a set of tourist spots in Hsinchu City, Taiwan is taken as an example. Currently, the tourist spot recognition system of this research can identify 28 tourist spots in Hsinchu. In addition to the attraction recognition feature, tourists can further use this tourist spot recognition system to obtain more information about 77 tourist spots from the Hsinchu City Government Information Open Data Platform, and then make dynamic travel itinerary planning and Google MAP navigation. Compared with other deep learning models using Faster region-convolutional neural networks or Single-Shot Multibox Detector algorithms for the same data set, the recognition time by the models using You Only Look Once version 3, Faster region-convolutional neural networks, and Single-Shot Multibox Detector algorithms are respectively 4.5, 5, and 9 s, and the mean average precision for each when IoU = 0.6 is 88.63%, 85%, and 43.19%, respectively. The performance experimental results of this research show the model using the You Only Look Once version 3 algorithm is more efficient and precise than the models using the Faster region-convolutional neural networks or the Single-Shot Multibox Detector algorithms, where You Only Look Once version 3 and Single-Shot Multibox Detector are one-stage learning architectures with efficient features, and Faster region-convolutional neural networks is a two-stage learning architecture with precise features.

Digital holography assisted with inline phase-shifting methods has the benefit of a large field of view and a high resolution, but it is limited in dynamic imaging due to sequential detection of multiple holograms. Here we propose and experimentally demonstrate a single-shot phase-shifting digital holography system based on a highly stable on-axis Fizeau-type polarization interferometry. The compact on-axis design of the system with the capability of instantaneous recording of multiple phase-shifted holograms and with robust stability features makes the technique a novel tool for the imaging of complex-valued dynamic objects. The efficacy of the approach is demonstrated experimentally by complex field imaging of various kinds of reflecting-type static and dynamic objects. Moreover, a quantitative analysis on the robust phase stability and sensitivity of the technique is evaluated by comparing the approach with conventional phase-shifting methods. The high phase stability and dynamic imaging potential of the technique are expected to make the system an ideal tool for quantitative phase imaging and real-time imaging of dynamic samples.

AbstractThe enormous growth of multimedia content in the field of the Internet of Things (IoT) leads to the challenge of processing multimedia streams in real-time. Event-based systems are constructed to process event streams. They cannot natively consume multimedia event types produced by the Internet of Multimedia Things (IoMT) generated data to answer multimedia-based user subscriptions. Machine learning-based techniques have enabled rapid progress in solving real-world problems and need to be optimised for the low response time of the multimedia event processing paradigm. In this paper, we describe a classifier construction approach for the training of online classifiers, that can handle dynamic subscriptions with low response time and provide reasonable accuracy for the multimedia event processing. We find that the current object detection methods can be configured dynamically for the construction of classifiers in real-time, by tuning hyperparameters even when training from scratch. Our experiments demonstrate that deep neural network-based object detection models, with hyperparameter tuning, can improve the performance within less training time for the answering of previously unknown user subscriptions. The results from this study show that the proposed online classifier training based model can achieve accuracy of 79.00% with 15-min of training and 84.28% with 1-hour training from scratch on a single GPU for the processing of multimedia events.

Room temperature linear arrays (up to 160 detectors in array) from silicon metal- oxide-semiconductor field-effect transistors (Si-MOSFETs) have been designed for sub- THz (radiation frequency 140 GHz) close to real-time direct detection operation scanner to be used for detection and recognition of hidden objects. For this scanner, the optical system with aspherical lenses has been designed and manufactured. To estimate the quality of optical system and its resolution, the system modulation transfer function was applied. The scanner can perform real-time imaging with the spatial resolution better than 5 mm at the radiation frequency 140 GHz and contrast 0.5 for the moving object speed up to 200 mm/s and the depth of field 20 mm. The average dynamic range of real time imaging system with 160-detector linear array is close to 35 dB, when the sources with the output radiation power of 23 mW (IMPATT diodes) are used (scan speed 200 mm/s). For the system with 32-detector array, the dynamic range was about 48 dB and for the single-detector system with raster scanning 80 dB with lock-in amplifier. However, in the latter case for obtaining the image with the sizes 20×40 mm and step of 1 mm, the average scanning time close to 15 min is needed. Convolutional neural network was exploited for automatic detection and recognition of hidden items.

In this article, a novel, efficient grasp synthesis method is introduced that can be used for closed-loop robotic grasping. Using only a single monocular camera, the proposed approach can detect contour information from an image in real time and then determine the precise position of an object to be grasped by matching its contour with a given template. This approach is much lighter than the currently prevailing methods, especially vision-based deep-learning techniques, in that it requires no prior training. With the use of the state-of-the-art techniques of edge detection, superpixel segmentation, and shape matching, our visual servoing method does not rely on accurate camera calibration or position control and is able to adapt to dynamic environments. Experiments show that the approach provides high levels of compliance, performance, and robustness under diverse experiment environments.

A suite of new collision avoidance systems (CAS) is presented for use in heavy vehicles whose structure and size necessarily impede driver visibility is introduced. The main goal of the project is to determine the appropriate use of each of the commercially available technologies and, where possible, produce a low cost variant suitable for use in proximity detection on large mining industry haul trucks. CAS variants produced were subjected to a field demonstration and, linked to the output from the earlier CAS 1 project, (a production high-definition in-cabin video monitor and r/f tagging system). The CAS 2 system used low cost Doppler continuous wave radar antennae coupled to the CAS 1 monitor to indicate the presence of an object moving at any speed above 3 Km/h relative to the antennae. The novelty of the CAS 3 system lies in the design of 3 interconnected, modules. The modules are 8 radar antennae (as used in CAS 2) modules located on the truck, software to interface with the end user (i.e. the drivers of the trucks) and a display unit. Modularisation enables the components to be independently tested, evaluated and replaced when in use. The radar antennae modules and the system as a whole are described together with the empirical tests conducted and results obtained. The tests, drawing on Monte-Carlo simulation techniques, demonstrate both the 'correctness' of the implementations and the effectiveness of the system. The results of the testing of the final prototype unit were highly successful both as a computer simulation level and in practical tests on light vehicles. A number of points, (as a consequence of the field test), are reviewed and their application to future projects discussed.

A suite of new collision avoidance systems (CAS) is presented for use in heavy vehicles whose structure and size necessarily impede driver visibility is introduced. The main goal of the project is to determine the appropriate use of each of the commercially available technologies and, where possible, produce a low cost variant suitable for use in proximity detection on large mining industry haul trucks. CAS variants produced were subjected to a field demonstration and, linked to the output from the earlier CAS 1 project, (a production high-definition in-cabin video monitor and r/f tagging system). The CAS 2 system used low cost Doppler continuous wave radar antennae coupled to the CAS 1 monitor to indicate the presence of an object moving at any speed above 3 Km/h relative to the antennae. The novelty of the CAS 3 system lies in the design of 3 interconnected, modules. The modules are 8 radar antennae (as used in CAS 2) modules located on the truck, software to interface with the end user (i.e. the drivers of the trucks) and a display unit. Modularisation enables the components to be independently tested, evaluated and replaced when in use. The radar antennae modules and the system as a whole are described together with the empirical tests conducted and results obtained. The tests, drawing on Monte-Carlo simulation techniques, demonstrate both the 'correctness' of the implementations and the effectiveness of the system. The results of the testing of the final prototype unit were highly successful both as a computer simulation level and in practical tests on light vehicles. A number of points, (as a consequence of the field test), are reviewed and their application to future projects discussed.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
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As autonomous vehicles begin to drive on the road, rational decision making is essential for driving safety and efficiency. The decision-making of autonomous vehicles is a difficult problem since it depends on the surrounding dynamic environment constraints and its own motion constraints. As the result of the combination of deep learning (DL) and reinforcement learning (RL), deep reinforcement learning (DRL) integrates DL's strong understanding of perception problems such as visual and semantic text, as well as the decision-making ability of RL. Hence, DRL can be used to solve complex problems in real scenarios. However, as an end-to-end method, DRL is inefficient and the final result tend to be poorly robust. Considering the usefulness of existing domain knowledge for autonomous vehicle decision-making, this paper uses domain knowledge to establish behavioral rules and combine rule-based behavior strategies with DRL methods, so that we can achieve efficient training of autonomous vehicle decision-making models and ensure the vehicle to chooses safe actions under unknown circumstances. First, the continuous decision-making problem of autonomous vehicles is modeled as a Markov decision process (MDP). Taking into account the influence of unknown intentions of other road vehicles on self-driving decisions, a recognition model of the behavioral intentions of other vehicles was established. Then, the linear dynamic model of the conventional vehicle is used to establish the relationship between the vehicle decision-making behavior and the motion trajectory. Finally, by designing the reward function of the MDP, we use a combination of RL and behavior rules-based controller, the expected driving behavior of the autonomous vehicle is obtained. In this paper, the simulation environment of scenes of intersections in urban roads and highways is established, and each situation is formalized as an RL problem. Meanwhile, a large number of numerical simulations were carried out, and the comparison of our method and the end-to-end form of DRL technology were discussed. "Due to its robust operation and high performance during bad weather conditions and overnight as well as the ability of using the Doppler Effect to measure directly the velocity of objects, the radar sensor is used in many application fields. Especially in automotive many radar sensors are used for the perception of the environment to increase the safety of the traffic. To increase the security level especially for vulnerable road users (VRU’s) like pedestrians or cyclists, radar sensors are used in driver assistance systems. Radar sensors are also used in the infrastructure, e.g. a commercial application is the detection of cars and pedestrians to manage traffic lights. Furthermore, radar sensors installed in the infrastructure are used in research projects for safeguarding future autonomous traffic. The object recognition and accuracy of radar-based sensing in the infrastructure can be increased by cooperating radar systems, which consist out of several sensors. This paper focus on the data fusion method of two radar sensors to increase the performance of detection and localization. For data fusion the high level cluster data of the two radar sensors are used as input data in a neuronal net (NN) structure. The results are compared to the localization obtained by using only a single radar sensor operating with an ordinary tracking algorithm. First, different models for chosen region of interests (ROI) and operating mode of cooperative sensors are developed and the data structure is discussed. In addition, the data are preprocessed with a coordinate transformation and time synchronization for both sensors, as well as the noise filtering to reduce the amount of clusters for the algorithm. Furthermore, three NN structures (CNN, DNN and LSTM) for static + dynamic objects and only dynamic objects are created, trained and discussed. Also, based on the results further improvements for the NN performance will be discussed."