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WiFi-based passive gesture tracking plays a crucial role in promoting human-computer interface, due to its pervasive availability and cost-effectiveness. Prior works focus on tracking gestures on 2D sensing plane by aggregating multiple WiFi links (typically 2), with Uniform Linear Arrays (ULAs). However, gestures actually contain 3D spatial information instead of just 2D, thus interpreting the 3D traces as 2D may lead to enormous tracking errors. This paper aims at exploring the possibility of passively tracking 3D hand traces likewise leveraging two WiFi links with standard 3-element ULAs, and presents a generic 3D centimeter-level passive gesture tracking system, called CentiTrack-3D. To this end, we make two key observations: (1) The ULA-resolved angle contains the integrated information of the azimuth and elevation in 3D space, despite its inability to estimate azimuth and elevation separately. (2) The radial motion deviating from the sensing plane also leads to length variations of paths. Motivated by the observations, a 3D tracking model named Chaos is elaborately designed to deduce the hand 3D coordinates, so as to track the traces. Extensive experiments yield that CentiTrack-3D achieves an overall median tracking granularity of 2.5 cm in 3D space in case of diverse users and environment conditions.

This article develops a novel cost function (performance index function) to overcome the obstacles in solving the optimal tracking control problem for a class of nonlinear systems with known system dynamics via adaptive dynamic programming (ADP) technique. For the traditional optimal control problems, the assumption that the controlled system has zero equilibrium is generally required to guarantee the finiteness of an infinite horizon cost function and a unique solution. In order to solve the optimal tracking control problem of nonlinear systems with nonzero equilibrium, a specific cost function related to tracking errors and their derivatives is designed in this article, in which the aforementioned assumption and related obstacles are removed and the controller design process is simplified. Finally, comparative simulations are conducted on an inverted pendulum system to illustrate the effectiveness and advantages of the proposed optimal tracking control strategy.

Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in realtime and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.

Connected and autonomous vehicles are expected to revolutionize transportation systems in the near future. Nevertheless, full autonomy still requires further development that will only be achieved through a high level of cooperation and coordination among vehicles. For the safe execution of several autonomous functions, vehicles will need to frequently communicate their current location to the Road Side Units (RSUs). However, due to information aging, it is expected that the delays from the time a message is generated until the location of the vehicle is updated will induce a tracking error. Thus, there is a tradeoff between the frequency of location updates and tracking error. Further, in order to reduce the queue-induced delays present in the system, the number of messages can be reduced by employing an event-triggered communication scheme. In this work, a family of optimization problems is proposed, utilizing the event-triggered algorithm, in order to determine the optimal triggering threshold. Both single and multiple RSU cases are considered in several simulation scenarios, demonstrating that the proposed optimization framework derives the optimal tracking accuracy. In the case of multiple RSUs, results are further optimized by assigning roads (and in turn vehicles) to RSUs if coverage areas of multiple RSUs overlap.

Multi-Panda Tracking (MPT) is a video-based tracking task for panda individuals, which is conducive to the observation and measurement of distribution and status of pandas. Different from tracking general objects such as pedestrians and vehicles, MPT is extremely challenging due to the indistinguishable appearances and diversified postures of pandas. In this case, existing tracking methods cannot appropriately tackle with the excessive occlusion between different panda individuals, hence suffering from identity switch, missing and inaccurate detections. To address these problems, we propose a simple yet effective MPT framework in the tracking-by-detection paradigm, which is benefited both from a short-term prediction filtering module and a discriminative feature learning network. In particular, the short-term prediction filtering module introduces similarity learning to enhance the temporal consistency among detections, which is capable of supplementing the missing detections and discarding false positive detections. Besides, the discriminative feature learning network leverages a two-branch network to learn both local and global discriminative features, so as to distinguish different panda individuals with a very similar appearance with a subtle difference. To evaluate the proposed method, we annotate a large-scale MPT dataset, named PANDA2021, which is particularly challenging due to the similar appearance and dramatic occlusion between panda individuals. Experiments on PANDA2021 demonstrate that the proposed MPT method significantly outperforms the competing methods. Moreover, experimental results on pedestrian tracking dataset MOT16 further demonstrate that the proposed MPT method achieves comparative performance with competing methods.

Recently, multi-task correlation filters has drawn much attention in the object tracking field, which utilizes the multi-task learning (MTL) approach to explore the interdependencies among deep features for object tracking. However, the existing multi-task correlation filters based method fails to consider the relations between the correlation filters. To address this problem, a novel correlation filters based visual tracking method is proposed in this paper, with the integration of multi-task convolution operators and object detection. In our method, convolution and correction filters are jointly learnt through using the MTL technique, with the purpose of exploring not only the interdependencies of deep features but also the internal relevance of the convolution filters. In addition, object detection is introduced into our algorithm to handle the problem of object missing to ensure a better performance of our tracking method. Experiments on five benchmark datasets demonstrate that the proposed visual tracking method outperforms existing state-of-the-art approaches.

In spatial augmented reality, a widely dynamic projection mapping system has been developed as a novel approach to graphics presentation for widely moving objects in dynamic situations. However, this method necessitates a novel tracking marker design that is resistant to random and complex occlusion and out-of-focus blurring, which conventional markers have not achieved. This article presents a uniform circumferential marker that becomes an ellipse in perspective projection and expresses geometric information. It can track the relative posture of a dynamically moving sphere with high speed, high accuracy, and robustness owing to continuous contour lines, thereby supporting both wide-range movement in the depth direction and human interaction. Moreover, a biased circumferential marker is proposed to embed unique coding, where the absolute posture is decoded with a novel recognition algorithm. Moreover, rough initialization using the geometry of multiple ellipses is proposed for both markers to start the automatic and precise tracking. Real-time rotation visualization onto the surface of a moving sphere is made possible with the high-speed, widely dynamic projection mapping system. The tracking performance is demonstrated to exhibit sufficient basic tracking performance as well as robustness against blurring and occlusion compared to conventional dot-based markers.

The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered tracking control approach is developed, where the next trigger instant is determined by the current information. Compared with the event-triggered control mechanism, its biggest advantage is that it does not need to continuously monitor the trigger condition of the system, which is convenient for physical realization. By the proposed controller, the hysteresis's effect can be compensated effectively and the tracking error can be bounded by an explicit function of design parameters. Simultaneously, all other signals in the closed-loop system can be remaining bounded. Finally, two examples are presented to verify the effectiveness of the proposed method.

This study investigates the bipartite fixed-time time-varying output formation-containment tracking issue for heterogeneous linear multiagent systems with multiple leaders. Both cooperative communication and antagonistic communication between neighbor agents are taken into account. First, the bipartite fixed-time compensator is put forward to estimate the convex hull of leaders' states. Different from the existing techniques, the proposed compensator has the following three highlights: 1) it is continuous without involving the sign function, and thus, the chattering phenomenon can be avoided; 2) its estimation can be achieved within a fixed time; and 3) the communication between neighbors can not only be cooperative but also be antagonistic. Note that the proposed compensator is dependent on the global information of network topology. To deal with this issue, the fully distributed adaptive bipartite fixed-time compensator is further proposed. It can estimate not only the convex hull of leaders' states but also the leaders' system matrices. Based on the proposed compensators, the distributed controllers are then developed such that the bipartite time-varying output formation-containment tracking can be achieved within a fixed time. Finally, two examples are given to illustrate the feasibility of the main theoretical findings.

This paper presents a deep learning-based method for defect detection and classification of power distribution lines using video analysis. The first stage is dataset preparation in which different videos are recorded. Then, a process is followed in such a way that a limited number of frames are used for processing, and power devices are detected in each frame using Faster R-CNN. Next, an equipment tracking technique is applied through the video frames. In the following, a method based on the classification of power equipment is presented. Then, the frame containing the closest component to the component in the median image is selected. In the selected frame, the area around the component is cut and given to AlexNet or GoogleNet to determine the equipment type and the severity level of defect is determined. Also, the defective equipment phase is determined based on the insulators detected in the video. The proposed method not only performs better than the state-of-the-art but also is a practical method, and with the least dependence on environmental conditions, can automatically identify defects in distribution lines, even in videos containing several possible defective devices.

Object tracking is a fundamental task for the visual perception system on the intelligent unmanned aerial vehicle (UAV). The high efficiency of correlation filter (CF) based trackers has advanced the widespread development of online UAV object tracking. This kind of method can effectively train a filter to discriminate the target from the background. However, most CF-based methods require a fixed label function over all the previous samples, leading to over-fitting and filter degradation, especially in complex drone scenarios. To address this problem, a novel adaptive response reasoning approach is proposed for CF learning. It can leverage temporal information in filter training and significantly promote the robustness of the tracker. Specifically, the proposed response reasoning method goes beyond the standard response consistency requirement and constructs an auxiliary label of the current sample. Besides, it helps learn a generic relationship between the previous and current filters, thereby realizing self-regulated filter updating and enhancing the discriminability of the filter. Extensive experiments on four well-known challenging UAV tracking benchmarks with 278 videos sequences show that the presented method yields superior results to 40 state-of-the-art trackers with real-time performance on a single CPU, which is suitable for UAV online tracking missions.

This paper addresses the event-triggered output feedback control problem of steer-by-wire (SbW) systems subject to uncertain nonlinearity and time-varying disturbance. First, to solve the uncertainty and remove unnecessary sensors, an interval 2 fuzzy logic system (IT2 FLS) and an adaptive state observer are proposed to estimate the uncertain nonlinearity and unavailable states of SbW systems. Then, an event-triggered output feedback control method is constructed for SbW systems to achieve the prespecified tracking performance. Much significantly, the jumping phenomenon of the control input caused by event-triggering communication can be eliminated by the proposed event-triggered control systems. Furthermore, theoretical analysis shows that the tracking error can converge to the preset neighborhood of origin within finite time, while the Zeno behavior can be avoided. Finally, simulations and vehicle experiments are presented to verify the validity of the proposed methods.

Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object Tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available at https://github.com/JustMeZXX/Intelligent-Night-Time- Traffic-Surveillance

Thermal sensors play an important role in intelligent transportation system. This paper studies the problem of RGB and thermal (RGBT) tracking in challenging situations by leveraging multimodal data. A RGBT object tracking method is proposed in correlation filter tracking framework based on short term historical information. Given the initial object bounding box, hierarchical convolutional neural network (CNN) is employed to extract features. The target is tracked for RGB and thermal modalities separately. Then the backward tracking is implemented in the two modalities. The difference between each pair is computed, which is an indicator of the tracking quality in each modality. Considering the temporal continuity of sequence frames, we also incorporate the history data into the weights computation to achieve a robust fusion of different source data. Experiments on three RGBT datasets show the proposed method achieves comparable results to state-of-the-art methods.

Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this paper proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints. The framework splits each frame into a tracked region, which is the region tracked from the previous video frame through optical flow analysis, and an emerging region, which is newly introduced into the scene due to the moving viewpoint. The framework subsequently extracts features based on the intensity profiles surrounding the boundaries of candidate shadow regions. These features are then utilized to both correct erroneous shadow boundaries for the tracked region and to detect shadow boundaries for the emerging region by a Bayesian learning module. To remove spurious shadows, spatial layout constraints are further considered for emerging regions. The experimental results demonstrate that the proposed framework outperforms the state-of-the-art shadow tracking and detection algorithms on a variety of challenging cases in real time, including shadows on backgrounds with complex textures, nonplanar shadows, fast-moving shadows with changing typologies, and shadows cast by nonrigid objects. The quantitative experiments show that our method outperforms the best existing method, achieving a 33.3% increase in the average F_{measure}. Coupled with an image-based shadow-casting method, the proposed framework generates realistic shadow interaction results. This capability will be particularly beneficial for supporting AR.

Pedestrians represent acutely complex and agile targets whose accident prevention has the highest priority concerning highly automated vehicles in urban traffic. Consequently, detection and tracking methods are necessary to determine motion behavior quickly and with high precision. With advancing microelectronics, powerful radar sensors and novel antenna structures are available for pedestrian detection systems, which can utilize unique features such as micro-Doppler signatures in several dimensions and high resolution. This paper presents a micro-Doppler based leg tracking framework that enables behavioral indications in a few sensor cycles. An adaptive motion model representing the kinematic locomotion of a human is derived to propagate the foot motion. Joint probabilistic data association assigns the detections to the respective leg and allows its temporal filtering of the position, micro-Doppler velocity, and kinematic parameters using an extended Kalman filter yielding real-time implementation capability. The presented approach provides the entire signal chain from raw radar data processing to extended multi-object state estimation based on highly relevant safety-critical motion maneuvers measured with radar parameterization corresponding to current serial short-range sensors.

This study addresses a finite-time prescribed performance controller within the concise fuzzy-neural framework with application to a waverider aircraft. Firstly, new finite-time performance functions are developed to construct a constraint funnel which accomplishes that tracking errors converge to their steady-state values in a given time (i.e., finite time convergence), being expected to guarantee tracking errors with small overshoots. Then, the equivalent transformation approach is introduced to unify unknown dynamics such that the control complexity is reduced. Moreover, to further reduce computational costs, a single-learning-parameter-based regulation scheme is developed for fuzzy-neural approximation. Finally, the proposed method is applied to a waverider aircraft to test its effectiveness and superiority.

Radar and camera information fusion sensing methods are used to solve the inherent shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the main hardware and camera as the auxiliary hardware framework. At the same time, the Mahalanobis distance is used to match the observed values of the target sequence. Data fusion based on the joint probability function method. Moreover, the algorithm was tested using actual sensor data collected from a vehicle, performing real-time environment perception. The test results show that radar and camera fusion algorithms perform better than single sensor environmental perception in severe weather, which can effectively reduce the missed detection rate of autonomous vehicle environment perception in severe weather. The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a $\bm {12\,\times 10\,\times \text{10-mm}}$ microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.

To shorten the scanning time by focusing on the local scanning for such specimens as cells, this paper proposes an advanced scanning strategy based on autonomous exploration, so as to detect the specimen in real time and achieve fast imaging for an atomic force microscopy (AFM). More specifically, fast raster scanning is first performed to locate the initial boundary point of the specimen. On this basis, a boundary tracking algorithm is proposed to construct the internal boundary of the specimen online through the autonomous exploration of the probe. Afterwards, the boundary is expanded according to the internal boundary tracking direction, based on which a convex hull of the specimen is further constructed for the local scanning. Furthermore, the local slow scanning is performed for the specimen according to the generated scanning trajectory. Subsequently, unscanned areas in the sample can also be scanned by this autonomous method. Experimental results verify that the proposed method can achieve fast scanning while ensuring high-quality imaging.

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

In active object tracking, given a visual observation as input, the goal is to lockup the target by autonomously adjusting camera’s position and posture. Previous works on active tracking assume that there is only one object (person) in the environment without distractors. In this work, towards realistic setting, we move forward to a more challenging scenario, where the tracker moves freely in 3D space like unmanned aerial vehicles (UAV) to track a person in various complex scenes with multiple distractors. To this end, we propose a novel end-to-end anti-distractor active object tracking framework by introducing multiple attention modules. On one hand, we take the target template to learn an embedding as channel-wise attention for current observation to distinguish the target from the distractors. On the other hand, temporal attention is introduced to fuse the observation history to extract a feature representation, which is then fed into a reinforcement learning network to output the action of the tracker. To evaluate our method, we build several multi-object 3D environments in Unreal Engine and extensive experiments demonstrate the effectiveness of our approach.

This paper proposes a new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors. Our tracker can better leverage object features for 3D Multi-Object Tracking (MOT) in point clouds. The proposed tracker is based on a novel data association scheme guided by prediction confidence, and it consists of two key parts. First, we design a new predictor that employs a constant acceleration (CA) motion model to estimate future positions, and outputs a prediction confidence to guide data association through increased awareness of detection quality. Second, we introduce a new aggregated pairwise cost to exploit features of objects in point clouds for faster and more accurate data association. The proposed cost consists of geometry, appearance and motion components. Specifically, we formulate the geometry cost using resolutions (lengths, widths and heights), centroids, and orientations of 3D bounding boxes (BBs), the appearance cost using appearance features from the deep learning-based detector backbone network, and the motion cost by associating different motion vectors. Extensive multi-object tracking experiments on the KITTI tracking benchmark demonstrated that our method outperforms, by a large margin, the state-of-the-art methods in both tracking accuracy and speed.

Gaze tracking is widely used in fatigue driving detection, eye disease diagnosis, mental illness diagnosis, website or advertising design, virtual reality, gaze-control devices and human-computer interaction. However, the influence of light, specular reflection and occlusion, the change of head pose, especially the ever-changing human pose in natural scenes, have brought great challenges to the accurate gaze tracking. In this paper, gaze tracking in natural scenes is studied, and a method based on Convolutional Neural Network (CNN) with residual blocks is proposed, in which attention mechanism is integrated into the network to improve the accuracy of gaze tracking. Furthermore, it is tested on the GazeFollow database which contains six kinds of databases. The results show that the performance of proposed method outperforms that of other state-of-the-art methods in natural scenes. Moreover, the proposed method has better real-time performance and is more suitable for practical applications.

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization.

The emergence of wearable devices has opened up new potentials for egocentric activity recognition. Although some methods integrate attention mechanisms into deep neural networks to capture fine-grained human-object interactions in a weak-supervision manner, they either ignore exploiting the temporal consistency or generate attention based on considering appearance cues only. To address these limitations, in this paper, we propose an enhanced attention-tracking method, combined with multi-branch network (EAT-MBNet), for egocentric activity recognition. Specifically, we propose class-aware attention maps (CAAMs) by employing a self-attention-based module to refine the class activation maps (CAMs). Our proposed method can enhance the semantic dependency between the activity categories and the feature maps. To highlight the discriminative features from the regions of interest across frames, we propose a flow-guided attention-tracking (F-AT) module, by simultaneously leveraging historical attention and motion patterns. Furthermore, we propose a cross-modality modeling branch based on an inter-active GRU module, which captures the time-synchronized long-term relationships between the appearance and motion branches. Experimental results on four egocentric activity benchmarks demonstrate that the proposed method achieves state-of-the-art performance.

Time-lapse microscopy images generated by biological experiments have been widely used for observing target activities, such as the motion trajectories and survival states. Based on these observations, biologists can conclude experimental results or present new hypotheses for several biological applications, i.e. virus research or drug design. Many methods or tools have been proposed in the past to observe cell and particle activities, which are formed as single cell tracking and single particle tracking problems, by using algorithms and deep learning technologies. In this article, a review for these works is presented in order to summarize the past methods and research topics at first, then points out the problems raised by these works, and finally proposes future research directions. The contributions of this article will help researchers to understand past development trends and further propose innovative technologies.

This letter shows the first acquisition, Doppler tracking, and positioning results with Starlinks low Earth orbit (LEO satellite signals. A generalized likelihood ratio (GLR -based test is proposed to acquire Starlinks downlink signals. A Kalman filter (KF -based algorithm for tracking the Doppler frequency from the unknown Starlink signals is developed. Experimental results show Doppler tracking of six Starlink satellites, achieving a horizontal positioning error of 10 m.

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.

This paper proposes a sensor network information fusion based distributed state estimation algorithm for tracking non-cooperative maneuvering target. In order to lower the communication burden and energy consumption, an event-triggered mechanism is introduced. The distributed state estimation algorithm for maneuvering target tracking is designed by two stages, namely, local filtering stage and consensus fusion stage. The algorithm proposed in this paper can be used to obtain the high accurate state estimation of the target by introducing a multiple suboptimal fading factor even when the target makes big maneuvering. Moreover, a contribution factor is designed in the information fusion stage to improve the accuracy of maneuvering target state estimation and reduce the consensus iteration times. Besides, the stochastic boundedness of the event-triggered distributed estimation algorithm is proved by introducing a stochastic process. Finally, Monte Carlo numerical simulation example is designed to illustrate the effectiveness of the algorithm.

We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.

The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects' identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of 102 μm, and 8.8° for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions.

In this paper, the finite-iteration tracking issue of multi-agent systems is investigated with the distributed optimization method. Since the infinite-iteration strategy is unrealistic, the finite-iteration approach is adopted to study the problem. Moreover, the tracking performance is undesired for the traditional iterative learning schemes. Then optimization technique is used to cover the drawback. The tracking error is regarded as the optimization objective, and the consensus tracking issue is considered as the distributed optimization problem. An appropriate learning control strategy is designed on the basis of the gradient of the tracking error. Further, the optimization issue can be solved by means of the finite-iteration approach, and the learning algorithm can improve the tracking performance. Theoretical results are proposed by using the norm estimation and some inequalities skills. At last, a numerical simulation is given to illustrate the effectiveness of the main results.

The robot-held laparoscope efficiently maintains the stability of the surgical screen in comparison with the laparoscope held by the assistant. The collaboration between the robot-held laparoscope with the surgeon is achieved without the artificial manipulation by tracking surgical instruments automatically. In this work, a visual tracking space vector method is proposed to autonomously control the field-of-view (FOV) by tracking multiple surgical instruments. In the proposed method, a visual tracking space vector is introduced to track the motion of multiple instruments, and a constraint vector is defined to ensure that the laparoscope is always within the safe working space. In addition, a modified YOLOv3 algorithm is proposed to robustly detect and classify the surgical instruments. The proposed method automatically adapts to the changes in the number of surgical instruments, as compared to other visual servoing methods for tracking surgical instruments. The proposed method does not require the depth information of the tracked surgical instruments. Experimental and simulation results verify that the proposed method accurately and efficiently controls the robot-held laparoscope to track the motion of multiple surgical instruments and ensure safe tracking movement.

This paper considers the problem of tracking the players in handball videos using a single video source. Tracking of handball players in the video is a difficult task as they can frequently leave and re-enter the camera field of view, often change directions quickly and occlude each other. Players wear similar team uniforms, play indoor under artificial illumination, with the background than can vary significantly as the handball court is often painted in various colors. The continually improving accuracy of CNN-based object detectors makes tracking-by-detection methods an attractive choice for tracking players in sports videos as they can perform online and with low computational requirements on top of object detection. Here we consider the use of three tracking-by-detection methods in conjunction with the YOLO object detector, namely the standard Hungarian assignment algorithm, the Simple Online, and Real-time Tracking (SORT) algorithm that adds a motion model, and its extension Deep SORT. The methods are tested on a custom dataset of handball video scenes.

To date, the majority of positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper, we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker’s mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e., new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.

Deblurring can not only provide visually more pleasant pictures and make photography more convenient, but also can improve the performance of objection detection as well as tracking. However, removing dynamic scene blur from images is a non-trivial task as it is difficult to model the non-uniform blur mathematically. Several methods first use single or multiple images to estimate optical flow (which is treated as an approximation of blur kernels) and then adopt non-blind deblurring algorithms to reconstruct the sharp images. However, these methods cannot be trained in an end-to-end manner and are usually computationally expensive. In this paper, we explore optical flow to remove dynamic scene blur by using the multi-scale spatially variant recurrent neural network (RNN). We utilize FlowNets to estimate optical flow from two consecutive images in different scales. The estimated optical flow provides the RNN weights in different scales so that the weights can better help RNNs to remove blur in the feature spaces. Finally, we develop a convolutional neural network (CNN) to restore the sharp images from the deblurred features. Both quantitative and qualitative evaluations on the benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of accuracy, speed, and model size., accepted by tcsvt

Pedestrian positioning with wearable devices is a significant application of attitude tracking. It tracks the attitude (with heading angle being the most important part) of the device in real time and provides positioning services for users based on the information of step length provided by Pedestrian Dead-Reckon (PDR), which is a cheap and efficient positioning method at present. However, amid a train of positioning methods, the joint estimate of tracking is given by a train of methods based on the direction of gravity and the earths magnetic field direction. Considering the measurement of gravity that the gravity accelerometer is exposed to heavy noise due to the complex movement of human body during walking with uniform swing arm posture and forward speed, this paper proposed a novel estimate method based on the Kalman filter with multi-state constraints and the usage of low-cost sensors, which fulfills the estimation with the sequential observation of magnetic field. Compared with other related work, this method proposed in this paper eliminates the dependence on gravity direction, avoiding the influence of heavy noise caused by additional linear acceleration in motion state, and reduces the influence of insufficient observation when using magnetic field observation alone. The performance of the proposed method is evaluated by real-world experimentation results.

This paper presents the first of its kind real-time simulation of the effect of spray water on an automotive LIDAR sensor. The simulation is based on physically measurable quantities in order to facilitate its validation and extension. Both the sensor and the environment are simulated using open software tools: Blender and Cycles, with the objective of facilitating standardization and broader adoption. The parameters required to construct and calibrate the simulation are extracted from real measurements done on a test area and highway covering different water film thicknesses and vehicle speeds. The simulation is validated in different stages, first by comparing the trajectories of the particles with a regular solver and secondly, by the spatial distribution of the virtual and real point clouds with the measurement results. The resulting framework can be easily expanded to cover scenarios that are more complex and other phenomena counterproductive to the performance of a LIDAR sensor like dirt, exhaust gases, snow, rain and fog. The virtually generated point clouds can be used to validate the performance of the software required for the segmentation, classification and tracking of objects generated by the LIDAR sensor, thereby reducing development costs and increasing software quality.