Multi-target Tracking Algorithm Research Articles

Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based on Labeled Multi-Bernoulli Filter

Optimizing Distributed Multi-Sensor Multi-Target Tracking Algorithm Based on Labeled Multi-Bernoulli Filter

Underwater Multi-Target Tracking Algorithm Based on Deep Learning

Underwater Multi-Target Tracking Algorithm Based on Deep Learning

Underwater Multitarget Tracking With Sonar Images Using Thresholded Sequential Monte Carlo Probability Hypothesis Density Algorithm

This letter presents a multi-target tracking algorithm—thresholded Sequential Monte Carlo probability hypothesis density (TH-SMC-PHD) algorithm. The TH-SMC-PHD aims to overcome the problem that underwater multi-target tracking is prone to missing tracking on sonar images, resulting in the breakage of trajectories. First, CA-CFAR and K-means are employed to detect potential underwater targets from sonar images, respectively. Then TH-SMC-PHD is applied to the detection results for tracking, using a continuously lost frame threshold to reduce the missing tracking rate and the Minimum-Sampling-Variance (MSV) resampling to improve tracking accuracy. An actual underwater multi-target tracking experiment using a forward-looking sonar was contracted in a water tank to evaluate the tracking performance. Compared with the other two PHD tracking algorithms, the results demonstrate that the proposed algorithm achieves high-precision, non-fracture, non-missing tracking of three targets. In addition, the TH-SMC-PHD is more stable and less affected by different detection algorithms, which has the potential for practical applications.

Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking

Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources. The first class, referred to as target-independent perception systems (TIPSs), consists of sensors that periodically produce noisy measurements of targets without requiring target cooperation. The second class, referred to as target-dependent reporting systems (TDRSs), relies on cooperative targets that report noisy measurements of their state and their identity. We present a joint TIPS-TDRS observation model that accounts for observation-origin uncertainty, missed detections, false alarms, and asynchronicity. We then establish a factor graph that represents this observation model along with a state evolution model including target identities. Finally, by executing the sum-product algorithm on that factor graph, we obtain a scalable multitarget tracking algorithm with inherent TIPS-TDRS fusion. The performance of the proposed algorithm is evaluated using simulated data as well as real data from a maritime surveillance experiment.

Multi-Target Tracking Algorithm Based on Improved Markov Random Field Model of Optical Flow Motion Constraint

Multi-Target Tracking Algorithm Based on Improved Markov Random Field Model of Optical Flow Motion Constraint

Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

A novel SMC-PHD filter for multi-target tracking without clustering

The probability hypothesis density (PHD) filter has been regarded as a practicable multi-target tracking algorithm which can alleviate the computational difficulty of the multi-target Bayes filter due to the multiple high-dimensional space integrals. As one of the major implementations of the PHD filter, sequential Monte Carlo PHD (SMC-PHD) is suitable for highly nonlinear systems. However, in scenarios with missing detections, the false-estimation problem occurs which leads to estimation performance degradation. To reduce the negative effects of missing detections, this paper develops a compensatory measurements generating mechanism and presents a novel measurement compensation based SMC-PHD filter, which can avoid the unreliable clustering. Comparative results verify the proposed filter, indicating good application prospects.

Estimations of time-varying birth cardinality distribution and birth intensity in Gaussian mixture CPHD filter for multi-target tracking

Cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However, the performance of standard CPHD is degraded when the target birth process is unknown and dynamically changing. To remove this limitation, a discrete kernel estimator in conjunction with exponential weighted moving average scheme is introduced in this study to estimate the time-varying target birth cardinality distribution (i.e., probability distribution on number of newborn targets appearing during one sampling time) at each processing step. The target birth intensity is then updated according to the resulting estimated birth cardinality distribution. The estimated birth intensity and cardinality distribution can be employed by a tracker based on Gaussian mixture CPHD (GMCPHD) to modulate its filtering strength for target tracking. The performance of the proposed framework is demonstrated by both numerical simulations and experiment on a video surveillance dataset. Results show that the proposed algorithm can reduce the delay of standard GMCPHD filter's response to the changes in the number of targets, and thus improve the accuracy of cardinality estimates. Satisfactory estimation of cardinality can be obtained even when there is a fast change in the rate of birth.

Epipolar Multitarget Velocity Probability Data Association Algorithm Based on the Movement Characteristics of Blasting Fragments

Fragments generated from the blast-fragmentation warhead after blasting are typically multiple, fast, small, and dense. In light of the epipolar multitarget feature of blasting fragments, this paper utilizes the movement characteristics of blasting fragments for modeling. Then, the modeling results are adopted in probabilistic data association (PDA) algorithm of multitarget tracking. A novel epipolar multitarget velocity PDA (VPDA) algorithm is proposed based on the movement characteristics of blasting fragments. This algorithm forms the movement characteristics with the finite element simulation results of warhead blasting fragments, utilizes the Doppler velocity probability to reassign the association probability, and updates the state and covariance of each target through the probability weighted fusion. Simulation results demonstrate that, the computational complexity of the proposed algorithm is close to that of PDA algorithm, and the association success rate and the state value update error approximates to the association effects of joint probabilistic data association (JPDA) algorithm, which can effectively track the fragments with identical velocity while reducing the complexity of the epipolar multitarget tracking algorithm, and can respond to the group target tracking scenario.

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.

Multi-Target Tracking Algorithm Based on 2-D Velocity Measurements Using Dual-Frequency Interferometric Radar

Multi-target tracking (MTT) generally requires either a network of Doppler radar receivers distributed at different locations or a phased array radar. The targets moving with small/no radial velocity or angular velocity only cannot be detected and localized completely by deploying Doppler radar without antenna arrays or multiple receivers. To resolve this issue, we present a new MTT algorithm based on 2-D velocity measurements, namely, radial and angular velocities, using dual-frequency interferometric radar. The contributions of the proposed research are twofold: First, we introduce the mathematical model and implementation of the proposed algorithm by explicitly establishing the relationship between 2-D velocity measurements and kinematic state of the target in terms of Cartesian coordinates. Based on 2-D velocity measurement function, the proposed MTT algorithm comprises the following steps: (i) data association using global nearest neighbor (GNN) method (ii) target state estimation using interacting multiple model (IMM) estimator combined with square-root cubature Kalman filter (SCKF) (iii) track management using rule-based M/N logic. Second, performance of the proposed algorithm is evaluated in terms of tracking accuracy, computational complexity and IMM mean model probabilities. Simulation results for different scenarios with multiple targets moving in different tracks have been presented to verify the effectiveness of the proposed algorithm.

Fusion-Based Multidetection Multitarget Tracking With Random Finite Sets

Multidetection (MD) systems are characterized by multiple observation modes (OMs), and hence, simultaneously produce multiple measurements for each target. The key challenge in exploiting MD systems for multitarget tracking (MTT), compared to single-detection (SD) systems, is the significant amount of extra computational burden involved in order to solve the resulting multidimensional assignment problem among measurements, targets, and OMs. This article presents a novel computationally efficient MTT framework for MD systems, wherein the multitarget state is modeled as a random finite set (RFS), and a bank of OM-dependent MTT RFS filters with SD model are employed to recursively provide OM-dependent posteriors. The latter, which contain both real and false targets, are then suitably fused so as to enhance consensus on the true targets while weakening trust on the existence of the false ones. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with the MD model. Two representative RFS filters, i.e., unlabeled probability hypothesis density (PHD) and labeled multi-Bernoulli (LMB), are considered in the proposed framework and the computational complexity of the resulting MD MTT algorithms is analyzed. Performance of the proposed approach is assessed by simulation experiments in both over-the-horizon-radar (OTHR) and single-frequency-network passive radar (SFN-PR) MTT applications.

Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss. Keywords: joint probability data association, pig tracking, particle filter, centroid DOI: 10.25165/j.ijabe.20211404.6105 Citation: Sun L Q, Li Y Y. Multi-target pig tracking algorithm based on joint probability data association and particle filter. Int J Agric & Biol Eng, 2021; 14(4): 199–207.

A Multi-Target Tracking and Detection Algorithm for Wireless Sensor Networks

There are a large number of sensor nodes in wireless sensor network, whose main function is to process data scientifically, so that it can better sense and cooperate. In the network coverage, it can comprehensively collect the main information of the monitoring object, and send the monitoring data through short-range wireless communication to the gateway. Although there are many applications in WSNs, a multi-Target tracking and detection algorithm and the optimization problem of the wireless sensor networks are discussed in this paper. It can be obviously seen from the simulation results that this node cooperative program using particle CBMeMBer filtering algorithm can perfectly handle multi-target tracking, even if the sensor model is seriously nonlinear. Simulation results show that the tracking - forecasting data association scheme applying GM-CBMeMBer, which is proposed in this paper, runs well in identifying multiple target state, and can improve the estimation accuracy of multiple target state.

The research of multi-target tracking based on improved YOLOv3

Multi-target tracking based on detection is a hot topic in the field of computer vision. Aiming at solving the problem of inconvenient application of large network model, this paper proposes a multi-target tracking algorithm based on improved YOLOv3. We use MobileNetV3 to replace the deep network as the backbone of YOLOv3. Compared with Faster R-CNN and YOLOv3, the model of MobileNetV3-YOLOv3 is greatly reduced and the detection speed is improved. Compared with the MobileNetV1-YOLOv3, the accuracy is improved and FPS is higher. The improved YOLOv3 model is used in DeepSORT algorithm for multi-target tracking. The experimental results show that the algorithm used in this paper has the best detecting and tracking effect.

An Improved Multi-target Tracking Algorithm for Automotive Radar

An improved multi-target tracking algorithm for automotive radar is proposed in this paper. In the application of automotive radar, in addition to the motion generated by the target maneuver, the maneuver of the radar-equipped vehicle will also cause the target to move relative to the radar. A single motion model for the target cannot accurately describe the motion state of the target relative to the radar. To solve the above problems, the nonlinear state equations of various motion models and their process noise covariance matrix are derived, and the Interacting Multiple Model (IMM) algorithm, the Joint Probabilistic Data Association (JPDA) algorithm and the Unscented Kalman Filter with Doppler measurement (DUKF) are combined. At the same time, the correlation coefficient of range measurement error and velocity measurement error of Frequency Modulated Continuous Wave (FMCW) radar is derived, and the relationship between it and FMCW radar parameters is studied. By simulation, its influence on target tracking is assessed, final results indicate the reference value for the application of automotive radar in intelligent driving.

Secure multitarget tracking over decentralized sensor networks with malicious cyber attacks

This paper is concerned with the multitarget tracking over decentralized sensor networks where the network can potentially be compromised by malicious cyber attacks. We consider the hybrid cyber attacks, including denial of service (DoS), false data injection (FDI), and extra packet injection (EPI) attack. We first establish the feature model of DoS, FDI and EPI attacks for decentralized multitarget tracking. Then, we propose a decentralized multitarget tracking algorithm against DoS, FDI and EPI attacks, which consists of three phases: prediction, adaptation and combination. The adaptation phase is to update the estimate of each node with its own measurements and all its neighbors' measurements. The combination phase is to fuse the estimate of each node with all its neighbors' estimates. By incorporation of the neighbors' measurements and fusing the neighbors' estimates, it can dramatically reduce the adverse effect of cyber attacks and provide reliable tracking performance. Numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.

Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking

The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi-target tracking (MTT) with unknown,time-varying number of targets. However, these methods can only be used in MTT systems with some prior information of multipletargets, such asdynamic model, newborn target distribution etc.;otherwise, the tracking performance will decline greatly. To solve this problem,an adaptive Grid-driven technique is proposed based on the framework of the PHD/CPHD filter to recursively estimate the target states without knowing the dynamic model and the newborn target distribution. The grid size can be adaptively adjusted according to the grid resolution, and the dynamic tendencies of the grids can respond to the unknown dynamic models of each targets, including arbitrary manoeuvring models. The newborn targets outside the grid area can be identified by analysing the measurements, and some new grids are generated around them. The experimental results show that the proposed algorithm has a better performance than the traditional particle filter-based PHD method in terms of average optimal sub-pattern assignment distance and average target number estimation for tracking multiple targets with unknown dynamic parameters and unknown newborn target distribution.

Detection and tracking of the trajectories of dynamic UAVs in restricted and cluttered environment

The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV’s initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm.

Multi-target Tracking Based on Deep Sort in Traffic Scene

In this paper, we use the deep sort multi-target tracking algorithm to achieve multi-target tracking of pedestrians and vehicles in traffic scenes. In this paper, firstly, yolov4 is used to train the pedestrian and vehicle detection model in traffic scenes. Then, according to the detection frame predicted by yolov4, multi-target tracking is carried out for specific targets. The multi-target tracking algorithm uses deep Sort, which can be combined with yolov4, can achieve less ID switching in real-time reasoning and deal with the loss of occlusion, so as to achieve more stable tracking effect.