Multi-target Tracking System Research Articles

A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking.

Most multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of different perspectives. Aiming to determine the influence of different perspectives on the fusion accuracy of multi-sensor tracking in the process of target tracking, this paper studies the multi-target tracking fusion strategy of a nonlinear system with different perspectives. A GM-JMNS-CPHD fusion technique is introduced for random outlier selection in multi-target tracking, leveraging sensors with limited views. By employing boundary segmentation from distinct perspectives, the posterior intensity function undergoes decomposition into multiple sub-intensities through SOS clustering. The distribution of target numbers within the respective regions is then characterized by the multi-Bernoulli reconstruction cardinal distribution. Simulation outcomes demonstrate the robustness and efficacy of this approach. In comparison to other algorithms, this method exhibits enhanced robustness even amidst a decreased detection probability and heightened clutter rates.

A reliable unmanned aerial vehicle multi-target tracking system with global motion compensation for monitoring Procapra przewalskii

Procapra przewalskii, which inhabits plateau areas, faces the constant threat of poaching and unpredictable risks that impede its survival. The implementation of a comprehensive, real-time monitoring and tracking system for Procapra przewalskii using artificial intelligence and unmanned aerial vehicle (UAV) technology is crucial to safeguard its existence. Therefore, a UAV multi-object-tracking (MOT) system with global motion compensation (GMC) was proposed in this study. YOLOv7 and Deep SORT were employed for object detection and tracking, respectively. Furthermore, the Kalman filter (KF) in Deep SORT is optimized to enhance the accuracy of object-tracking. Moreover, a novel appearance feature-extraction network (FEN) is introduced to enable more effective multi-scale feature (MSF) extraction. In addition, a GMC module was proposed to align neighboring frames through feature matching. This facilitates the correction of the position of the target in the subsequent frame, mitigating the impact of UAV camera motion on tracking. The results demonstrated the remarkable tracking accuracy of the system. Compared with the Deep SORT model, the proposed system exhibited an increase of 6.4% in MOTA, 2.7% in MOTP, and 7.9% in IDF1. Through a comprehensive evaluation and analysis of real-world tracking scenarios, the system proposed in this study exhibits reliability in complex scenes and holds the potential to significantly enhance the protection of Procapra przewalskii from threats.

Magnetic Characterization of Human Intrinsically Magnetic Monocytes Through a Novel Optical Tracking-Based Magnetic Sensor.

Intrinsically magnetic cells naturally occur within organisms and are believed to be linked to iron metabolism and certain cellular functions while the functional significance of this magnetism is largely unexplored. To better understand this property, an approach named Optical Tracking-based Magnetic Sensor (OTMS) has been developed. This multi-target tracking system is designed to measure the magnetic moment of individual cells. The OTMS generates a tunable magnetic field and induces movement in magnetic cells that are subsequently analyzed through a learning-based tracking-by-detection system. The magnetic moment of numerous cells can be calculated simultaneously, thereby providing a quantitative tool to assess cellular magnetic properties within populations. Upon deploying the OTMS, a stable population of magnetic cells in human peripheral monocytes is discovered. Further application in the analysis of clinical blood samples reveals an intriguing pattern: the proportion of magnetic monocytes differs significantly between systemic lupus erythematosus (SLE) patients and healthy volunteers. This variation is positively correlated with disease activity, a trend not observed in patients with rheumatoid arthritis (RA). The study, therefore, presents a new frontier in the investigation of the magnetic characteristics of naturally occurring magnetic cells, opening the door to potential diagnostic and therapeutic applications that leverage cellular magnetism.

Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles.

Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.

Mission performance analysis of phased-mission systems with cross-phase competing failures

Phased-mission systems (PMSs) with sophisticated failure modes could behave diverse mission performance levels. The non-negligible competing failures, resulting from complicated components’ failure patterns, have posed a great challenge for the mission performance analysis of PMSs. Even though many methods have been developed to facilitate reliability assessment of PMSs with competing failures in the past few decades, the mission performance analysis of PMSs with competing failures, varying from phase to phase, is still in difficulty. This feature is defined as the cross-phase competing failure, characterized by a two-by-two crossover combination of three or more failure modes in distinct mission phases. In this study, mission performance analysis of PMSs with cross-phase competing failures is conducted. By the divide-and-conquer strategy, the conditional probability of paths, resulting from the phase-dependent effects of components, is computed via an iterative method. Based on the phase equivalent binary decision diagrams, the mission performance analysis of PMSs with cross-phase competing failures is conducted. The probability distribution of mission performance levels, together with the system reliability, is assessed phase-by-phase based on the lifetime distributions of components and mission durations. A distributed multi-sensor multi-target tracking system, along with a set of comparative studies, is given to demonstrate the effectiveness and applicability of the proposed method. The results show that the proposed approach to mission performance analysis of PMSs is more computationally efficient than the existing combinational models.

A robust attribute-aware and real-time multi-target multi-camera tracking system using multi-scale enriched features and hierarchical clustering

A robust attribute-aware and real-time multi-target multi-camera tracking system using multi-scale enriched features and hierarchical clustering

Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace

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.

Prototype development of multi-target tracking system for space multi-node laser communication network

Laser communication network is the future trend, but the disadvantage of a traditional laser communication system is that it can only transmit with a single node. To solve this problem, we propose a multi-target tracking technology to simultaneously establish multiple laser communication links. This study also develops a multi-target detection method based on beacon modulation and code division multiple access, proposes an optical antenna central stabilization method, and enhances the controller of the fast steering mirror (FSM) to improve the tracking performance. To verify the feasibility of our proposed method, we build a prototype system for multi-target laser communication and conduct the corresponding experiment. The experiment simulates the multi-target tracking situation by three beam emitting subsystems, and the dynamic tracking performance of the prototype system is verified by applying perturbations through a shaking table. Results show that the proposed method can effectively extract the position of the target in the mixed beam, with an average detection deviation of 2.95 μrad compared with when only a single beam is incident. The introduction of central stabilization enhances the dynamic tracking performance of the optical antenna. Tracking accuracy of the optical antenna is 24.63 μrad when attitude disturbance is applied. The improved controller enhances the performance of FSM, composite tracking accuracy is 5.69 μrad, and tracking error is reduced by 22.16% compared with the traditional PI controller. Performance also improved by 45.42% compared with when there was no improvement. Experimental results verify the effectiveness of the multi-target tracking system. On the basis of the proposed method, we achieve simultaneous tracking of multiple targets in different directions and significantly improve the dynamic tracking performance.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

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.

Soccer Player Video Target Tracking Based on Deep Learning

Soccer player video target tracking is a very challenging task, which has good practical and commercial value. Traditional soccer game target tracking relies on athletes to carry a recording chip to achieve target tracking, but the cost is very high. With the rapid development of photography technology and deep learning technology, athletes’ target tracking is realized through soccer game video. Deep learning technology is applied to computer vision detection and tracking. How to realize soccer players’ video target tracking under deep learning is a challenging lesson. To solve this problem, this paper takes the video target tracking of football players as the research object, collects the game images of the stadium through multiple cameras, realizes the long-term accurate tracking of multiple players, and establishes a multicamera multitarget tracking system. The KCF algorithm and the improved KCF algorithm formed by replacing the hog feature of the KCF algorithm with the depth convolution neural network are used to compare and analyze the impact of different target tracking ranges and target numbers on the target tracking accuracy of the system, so as to accurately obtain the motion trajectory of football players. The results show that the image data of football matches are collected independently by multiple cameras, and the data of multiple cameras are collected to generate each target motion datum. The KCF algorithm of multicamera multitarget tracking has good robustness and real-time for long-term accurate tracking of football players; the KCF algorithm and the improved KCF algorithm have high accuracy in target tracking. With the increase of tracking frame range, the accuracy of target tracking of the two algorithms is improved. At the same time, multitarget tracking helps to improve the antiocclusion ability of the system. The research results have important practical significance and good application prospects for the analysis technology of video content of football matches.

Real-Time Multitarget Tracking for Panoramic Video Based on Dual Neural Networks for Multisensor Information Fusion

A multitarget real-time tracking system for panoramic video with multisensor information fusion dual neural networks is studied and implemented by combining dual neural networks, fused geometric features, and deep learning-based target real-time tracking algorithms. The motion model of multisensor information fusion is analyzed, the dual neural network perturbation model is introduced, and the state variables of the system are determined. Combined with the data structure model of multisensor information fusion, a least-squares-based optimization function is constructed. In this optimization function, the image sensor is constructed as an observationally constrained residual term based on the pixel plane, the inertial measurement unit is constructed as an observationally constrained residual term based on the motion model, and the motion relationship between sensors is constructed as a motion-constrained residual term to derive a mathematical model for real-time multitarget tracking of panoramic video. The simulation experimental results show that the integrated position estimation accuracy of the multisensor information fusion dual neural network-based panoramic video multitarget real-time tracking fusion algorithm is 10.5% higher than that of the optimal distributed panoramic video multitarget fusion algorithm without feedback. The results of the simulation experiments show that the integrated position estimation accuracy of the multitarget real-time tracking fusion algorithm for panoramic video improves by 10.96%, and the integrated speed estimation accuracy improves by 6.32% compared with the optimal distributed multitarget fusion algorithm without feedback. The proposed panoramic video multitarget real-time tracking algorithm based on the dual neural network can effectively improve the target tracking accuracy of the model on degraded frames (motion blur, target occlusion, out-of-focus, etc.), and the stability of the algorithm for target location and category detection is effectively improved by multiframe feature fusion. The research in this paper provides better technical support and research theory for panoramic video multitarget real-time tracking.

Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

AbstractWith the advancements in 5G network and mobile edge computing technology, autonomous vehicle technology has gained new development opportunities. Multitarget tracking becomes the research hotspots in autonomous vehicles. Since many factors such as motion blur, partial occlusion, and illumination changes affect the performance of target tracking, the problem of target tracking is still an open topic. In this article, inspired by the strong discriminative ability of dictionary learning, the discriminative dictionary learning with embedded auto‐encoder (DDLEA) algorithm is developed for the multitarget tracking system. The DDLEA algorithm integrates the auto‐encoder into the dictionary learning framework and learns sparse representations while preserving the local structure and discriminative information of data. The learned dictionary model has the strong recognition ability. Further, a multitarget tracking system is developed based on the proposed DDLEA algorithm and the hierarchical data association scheme. Based on the target confidence in the STKSVD model, the hierarchical data association method first uses the Hungarian algorithm to complete the preliminary matching of high confidence targets, and then further tracks the low confidence targets to improve the tracking ability. Experiments are carried out the public MOT 2015 dataset. Compared with several popular multitarget tracking algorithms, the tracking performance of our system is satisfactory.

Application of Adaptive UKF Algorithm in Multi-target Tracking and Positioning System

Adaptive filtering algorithm (FIR) is a design method of adaptive variable target tracking system based on probability density distribution model. The algorithm realizes the target movement in the global range by estimating the parameters of different regions in the image, which improves the real-time performance and effectiveness.

Integrating Covariance Intersection into Bayesian multi-target tracking filters

Multi-target tracking systems typically provide sets of estimated target states as their output. It is challenging to be able to integrate these outputs as inputs to other tracking systems to gain a better picture of the area under surveillance since they do not conform to the standard observation model. Moreover, in cyclic distributed systems, there may be common information between state estimates that would mean that fused estimates may become overconfident and corrupt the system. In this paper we develop a Bayesian multi-target estimator based on the covariance intersection algorithm for multi-target track-to-track data fusion. The approach is integrated into a multi-target tracking algorithm and demonstrated in simulations. The approach is able to account for missed tracks and false tracks produced by another tracking system.

Autonomous angles-only multitarget tracking for spacecraft swarms

This paper presents a new algorithm for autonomous multitarget tracking of resident space objects using optical angles-only measurements from a spaceborne observer. To enable autonomous angles-only navigation of spacecraft swarms, observers must identify and track multiple known or unknown target space objects in view, without reliance on a-priori relative orbit knowledge. Extremely high tracking precision is necessary despite low measurement frequencies and limited computational resources. The new ’Spacecraft Angles-only MUltitarget tracking System’ (SAMUS) algorithm has been developed to meet these objectives and constraints. It combines domain-specific modeling of target kinematics with multi-hypothesis techniques to autonomously track multiple unknown targets using only sequential camera images. A measurement transform ensures that target motion in the observer reference frame follows a consistent parametric model; curve fitting is used to predict track behavior; and kinematic track gating and scoring criteria improve the efficiency and accuracy of the multi-hypothesis approach. Monte Carlo testing with high-fidelity simulations demonstrates close to 100% data association precision and high recall across a range of multi-spacecraft formations, in both near-circular and eccentric orbits. Tracking is maintained in the presence of eclipse periods, significant measurement noise, and partially known swarm maneuvers. A comparison to other tracking algorithms reveals strong advantages in precision, robustness and computation time, crucial for spaceborne angles-only navigation.

Convex Variational Inference for Multi-Hypothesis Fractional Belief Propagation Based Data Association in Multiple Target Tracking Systems

The success of Multi-hypothesis tracking(MHT) lies in the use of multiple scans, which can often yield improved tracking performance over single scan based data association methods in radar-centric multi-target tracking(MTT) systems. However, with the increase of the number of targets associated with measurements, there is an exponentially increasing need for formulating potential hypotheses, of which the computational cost may be prohibitively expensive. In this paper, a multi-hypothesis fractional belief propagation (MHFBP) based data association algorithm is proposed by the use of a probabilistic graph model for both the previous trajectories and the current measurements. To achieve this idea, there are two main steps. First, the trajectory-related indicators associated state variables are created by making use of a convex fractional free energy (FFE)function. Second, the convex optimization algorithm is used for the objective function and the fractional belief propagation (FBP) is exploited to obtain the best marginal belief of measurement for target association. In this way, incorrect hypotheses with extremely low probability can be eliminated. Finally, the proposed method is applied to multiple scenarios for MTT by indoor radar system. From the results, we can observe that it provides higher tracking performance compared with the classical MHT, feature-aided MHT(FA-MHT) and MHT-belief propagation (MHT-BP). In addition, we see that the computational burden of the proposed method is reduced significantly to avoid the phenomenon of exponential growth with increasing the number of targets.

First experimental evaluation of multi-target multileaf collimator tracking during volumetric modulated arc therapy for locally advanced prostate cancer

PurposeLocally advanced and oligometastatic cancer patients require radiotherapy treatment to multiple independently moving targets. There is no existing commercial solution that can simultaneously track and treat multiple targets. This study experimentally implemented and evaluated a real-time multi-target tracking system for locally advanced prostate cancer. MethodsReal-time multi-target MLC tracking was integrated with 3D x-ray image guidance on a standard linac. Three locally advanced prostate cancer treatment plans were delivered to a static lymph node phantom and dynamic prostate phantom that reproduced three prostate trajectories. Treatments were delivered using multi-target MLC tracking, single-target MLC tracking, and no tracking. Doses were measured using Gafchromic film placed in the dynamic and static phantoms. Dosimetric error was quantified by the 2%/2 mm gamma failure rate. Geometric error was evaluated as the misalignment between target and aperture positions. The multi-target tracking system latency was measured. ResultsThe mean (range) gamma failure rates for the prostate and lymph nodes, were 18.6% (5.2%, 28.5%) and 7.5% (1.1%, 13.7%) with multi-target tracking, 7.9% (0.7%, 15.4%) and 37.8% (18.0%, 57.9%) with single-target tracking, and 38.1% (0.6%, 75.3%) and 37.2% (29%, 45.3%) without tracking. Multi-target tracking had the lowest geometric error with means and standard deviations within 0.2 ± 1.5 for the prostate and 0.0 ± 0.3 mm for the lymph nodes. The latency was 730 ± 20 ms. ConclusionThis study presented the first experimental implementation of multi-target tracking to independently track prostate and lymph node displacement during VMAT. Multi-target tracking reduced dosimetric and geometric errors compared to single-target tracking and no tracking.

A novel multi-target multi-camera tracking approach based on feature grouping

Multi-target multi-camera tracking systems track many pedestrians through videos taken from multiple cameras. Generally, multi-target multi-camera tracking comprises three steps, namely, detection, feature extraction, and data association. It also involves a number of marginal post-processing procedures, such as pruning and interpolating. The task is a complicated and challenging problem. In this work, we mainly focus on the process of data association. When correlation clustering-based algorithms are adopted in data association, serious information loss may be observed, especially when pedestrians in a video run into occlusion. Thus, we propose a method called feature group which mitigates the decline in accuracy under occlusions. The proposed method is intuitional but easy to implement without changing the original framework. After comprehensive experiments, the proposed method is proved effective and is able to make substantial improvements on the DukeMTMC dataset. The feature group method is also competitive relative to other state-of-the-art methods.

Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection

In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-known fusion method that produces a conservative estimate of the joint covariance regardless of the actual correlation between the different nodes. Inverse covariance intersection (ICI) is the updated version to obtain fusion results that guarantee consistency and less conservative than CI. However, the ICI is not extended to multi-sensor multi-target tracking system yet. Since the ICI formula can be re-structured as naïve fusion with covariance inflation in Gaussian pdf, this method was applied to the GM-CPHD with generalization. The formula for random finite set (RFS) fusion was derived in the same way as the conventional generalized covariance intersection (GCI) based fusion. The simulation results for multi-target tracking show that the proposed algorithm has smaller optimal sub-pattern assignment (OSPA) errors than naïve fusion and the GCI-based fusions.