Multiple Target Tracking Research Articles

Multidimensional morphological analysis of live sperm based on multiple-target tracking

Manual semen evaluation methods are subjective and time-consuming. In this study, a deep learning algorithmic framework was designed to enable non-invasive multidimensional morphological analysis of live sperm in motion, improve current clinical sperm morphology testing methods, and significantly contribute to the advancement of assisted reproductive technologies. We improved the FairMOT tracking algorithm by incorporating the distance and angle of the same sperm head movement in adjacent frames, as well as the head target detection frame IOU value, into the cost function of the Hungarian matching algorithm. For sperm morphology, we used the BlendMask segmentation method to segment individual sperm. SegNet was used to separate the head, midpiece, and principal piece comments from each sperm. Experienced in vivo sperm physicians confirmed a morphological accuracy percentage of 90.82%. A total of 1272 samples were collected from multiple tertiary hospitals for validation of the system, which were also evaluated by physicians. The results of our system were highly consistent with those of manual microscopy. This study realized the automated detection of progressive motility and morphology of sperm simultaneously, which is crucial for selection of morphologically normal and motile sperm for intracytoplasmic sperm injection.

Distributed Joint Detection, Tracking, and Classification via Labeled Multi-Bernoulli Filtering.

In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each individual node and data fusion among multiple nodes. For local filtering, the LMB filter is extended to JDTC by augmenting the target state to incorporate class and mode information. Further, the well-known generalized covariance intersection and recently developed minimum information loss fusion paradigms are exploited for data fusion among sensors. The effectiveness of the resulting algorithm, called D-JDTC-LMB, is assessed via simulation experiments.

Thermal Image Tracking for Search and Rescue Missions with a Drone

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for the measurement-to-track association. This proposed rule is combined with the statistically nearest neighbor association rule to assign measurements to tracks. The track-to-track association selects the fittest track for a track and fuses them. In the experiments, three videos of three hikers simulating being lost in the mountains were captured using a thermal imaging camera on a drone. Capturing was assumed under difficult conditions; the objects are close or occluded, and the drone flies arbitrarily in horizontal and vertical directions. Robust tracking results were obtained in terms of average total track life and average track purity, whereas the average mean track life was shortened in harsh searching environments.

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

AbstractPoisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

Automatic Navigation of a Microrobotic Swarm for Tracking Multiple Mobile Targets

Microrobotic swarms actuated by magnetic fields have attracted considerable attention in the field of automatic control. Developing effective control schemes for microrobotic swarms to track multiple mobile targets is still a major challenge. In this work, we propose an optimized rapidly-exploring random tree star (ORRT*) algorithm to generate desired paths connecting the swarm and the targets. A path selection strategy is developed to select the shortest path, and subsequently the immediate target to track is determined. The sliding mode (SM) motion controller is then presented to achieve the tracking of multiple mobile targets using microrobotic swarms. Comparisons are performed to validate the effectiveness of the control scheme, including the path planner and the motion controller. Finally, simulations and experiments of tracking multiple mobile targets in a virtual micromaze are conducted.

Dynamic Spectrum Tracking of Multiple Targets With Time-Sparse Frequency-Hopping Signals

Dynamic Spectrum Tracking of Multiple Targets With Time-Sparse Frequency-Hopping Signals

Enhanced Multiple Target Tracking Using a Generalized Multi-Target Smoothing Algorithm With Tracklet Association

Enhanced Multiple Target Tracking Using a Generalized Multi-Target Smoothing Algorithm With Tracklet Association

Multi-Dimensional Resource Management Scheme for Multiple Target Tracking Under Dynamic Electromagnetic Environment

Multi-Dimensional Resource Management Scheme for Multiple Target Tracking Under Dynamic Electromagnetic Environment

Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Domain-knowledge-aided airborne ground moving targets tracking

This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving target tracking algorithm. To improve the accuracy of multiple target tracking, the proposed algorithm incorporates domain knowledge into the joint probabilistic data association (JPDA) filter as state constraints. The domain knowledge considered in this paper includes both road information extracted from a given map and local traffic regulations. Conventional track update method is modified to enhance the capability of recognition of temporarily track loss. A variable structure multiple model (VS-MM) method is developed to assign the road segment to a given target. The main contribution of this paper is that we establish a comprehensive framework for target tracking with the aid of domain knowledge and analyze the effect of diverse constraints on target tracking performance, while the limitations involve only 2-D target behaviors are considered, and the sophisticated interactions between targets are neglected. The effectiveness of proposed algorithm is demonstrated through extensive numerical simulations.

Multiple maneuvering target tracking based on hierarchical Dirichlet process and hidden Markov model

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets.

Robust Tracking of Multiple Targets Subspace Based on Distributed Array Networks

Robust subspace tracking is a real-time solution for suppressing harsh interference and estimating the target subspace in complex electromagnetic environments. In this paper, a robust and decentralized subspace tracking method is proposed. The multi-sensor array is divided into a number of nodes, each attached with a processor and no central processing unit (CPU) is deployed. In order to reduce the impact of burst pulse, a robust real-time update algorithm of the subspace is designed through the weighted least squares method. The optimization model established in this paper is robust to synchronization error. Then a subspace real-time update algorithm is proposed based on the average consensus (AC) method that integrates the information of distributed arrays. The computational complexity and convergence properties of the proposed algorithm are also investigated. Simulation results show that the proposed approach converges to the subspace estimation error by an order of magnitude compared with the single-node algorithm.

A SISO FMCW radar based on inherently frequency scanning antennas for 2-D indoor tracking of multiple subjects

The contextual non-invasive monitoring and tracking of multiple human targets for health and surveillance purposes is an increasingly investigated application. Radars are good candidates, since they are able to remotely monitor people without raising privacy concerns. However, radar systems are typically based on complex architectures involving multiple channels and antennas, such as multiple-input and multiple-output (MIMO) or electronic beam scanning, resulting also in a high power consumption. In contrast with existing technologies, this paper proposes a single-input and single-output (SISO) frequency-modulated continuous wave (FMCW) radar in combination with frequency scanning antennas for tracking multiple subjects in indoor environments. A data processing method is also presented for angular separation and clutter removal. The system was successfully tested in five realistic indoor scenarios involving paired subjects, which were either static or moving along predefined paths varying their range and angular position. In all scenarios, the radar was able to track the targets, reporting a maximum mean absolute error (MAE) of 20 cm and 5.64^circ in range and angle, respectively. Practical applications arise for ambient assisted living, telemedicine, smart building applications and surveillance.

Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.

MMF-GSTIW-PMBM Adaptive Filter for Multiple Group Target Tracking With Heavy-Tailed Noise

MMF-GSTIW-PMBM Adaptive Filter for Multiple Group Target Tracking With Heavy-Tailed Noise

Resource Optimization Strategy in Phased Array Radar Network for Multiple Target Tracking When Against Active Oppressive Interference

The phased array radar network has the ability to track multiple targets simultaneously through the cooperative work of several active nodes, which brings significant advantages in tracking target under oppressive interference. In this article, an efficient resource optimization strategy (ROS) is developed in this scenario. The optimization model is formulated with the aim of countering active oppressive interference by dynamically assigning tracking tasks and allocating transmit power to each node. The architecture of information fusion considered in this article is centralized so that the detection performance and the parameter estimation accuracy can be improved. Since the predicted conditional Cramer–Rao lower bound (PC-CRLB) can provide an accurate performance lower bound for target tracking accuracy, the objective function is designed to improve the sum of weighted PC-CRLBs of multiple targets. By introducing the relaxation technique and the sorting algorithm, this mixed integer nonlinear programming problem is converted into a two-stage nonconvex problem, and the efficient solutions are obtained through the modified Zoutendijk method of feasible directions algorithm. Numerical simulations demonstrate the effectiveness of the proposed ROS.

Pareto-based bi-objective optimization for robust power allocation in hybrid MIMO phased-array radar system under air defense maneuvering tracking

Recently, Hybrid MIMO phased-array (H-MIMO-PA) radar systems have been demonstrated to offer enhanced target tracking capabilities. In practice, an effective radar resource allocation (RA) strategy can ensure the quality of service (QoS) of subtasks and improve the system resource utilization in multiple target tracking (MTT) applications. In this paper, a robust power allocation strategy is developed with the objectives of enhancing target tracking accuracy (TTA) and low probability of intercept (LPI) performance in H-MIMO-PA radar. Firstly, in order to accurately grasp the air defense battlefield situation and improve the QoS for MTT, we propose a threat assessment model based on the intuitionistic fuzzy entropy (IFE) to calculate the comprehensive threat degree of each target in real time. Next, since the predicted conditional Cramér-Rao lower bound (PC-CRLB) is based on the most recently realized measurement and provides an accurate lower bound, the PC-CRLB in clutter is derived and utilized to formulate the task utility function for TTA. In addition, the probability of intercept is derived and adopted as the performance metric for LPI. Due to power allocation process may become conflicting for achieving better TTA and LPI performance at the same time, the Pareto-based bi-objective optimization scheme is adopted, which formulates a non-convex optimization problem. Finally, to solve the formulated model, we propose the modified bi-objective particle swarm optimization (MBOPSO) algorithm. Simulation results are provided to verify the effectiveness of the proposed power allocation algorithm.

Tracking of Multiple Targets Using Interactive Multiple Model and Data Association Filter

Tracking of multiple targets using interactive multiple model and data association filter has been developed on PC MA TLAB. The performance of the algorithm is evaluated in terms of estimated and measured tracks in presence of clutter, mode probability, standard deviation, root mean square position error and with varieties of simulated scenarios. Sensitivity of the algorithm to the choice of tracking models and process noise covariance values has been studied. This algorithm has also been evaluated for multi sensor multi target scenario using fusion technique. It was observed that interactive multiple model probabilistic data association filter gives a realistic confidence in the estimates during maneuvers and lower RSSPE during the non-maneuvering phase of the targets.

A Fast Solver for Dwell Time Allocation in a Phased Array Radar

The dwell time resource is usually limited when a phased array radar (PAR) tracks multiple targets. In this paper, a fast solver is proposed for the dwell time allocation (DTA) in the cluttered environments. The optimization model is established as minimizing the worst case of position Bayesian Cramér-Rao lower bounds (BCRLBs) among multiple targets under the constraint of total dwell time budget. The BCRLB minimization is hindered by the intractable information reduction factor (IRF) term, which expresses the information loss due to clutter. By replacing the IRF with its upper bound, the probability of detection (PD), we formulate a closed-form objective function-based optimization problem. By using the strictly decreasing property of the BCRLB, two optimal conditions are proposed to obtain a unique subset of the optimal solutions. Then, the remaining parts can be directly set as the lower or upper bounds. Simulation results confirm the competitive performance and computation saving, compared with existing convex optimization-based methods and the sequential relaxation-based method, especially when facing a large number of targets. The target with a lower signal-to-noise ratio (SNR) will be given more dwell time resources.

PMBM Filter for Multiple Extended Targets With Unknown Clutter Rate and Detection Probability

In a multi-target tracking (MTT) scenario, the prior knowledge of parameters such as clutter rate and detection probability are usually uncertain or estimated offline from training data. However, significant parameters mismatch in the clutter and detection model will result in biased estimates. In such cases, the ability to adaptively online estimate clutter rate and detection probability is critical in MTT under the random finite sets (RFS) framework. In this paper, we propose a robust Poisson multi-Bernoulli mixture (PMBM) filter that can accommodate model mismatch in clutter rate and detection probability for multiple extended target tracking problems. Moreover, the closed-form solution to the proposed method is derived by the use of Beta and Gamma Gaussian inverse-Wishart (GGIW) distribution, where Beta is used to describe unknown detection probability and GGIW is used to model extended target extension as an ellipse. Simulation results are presented to verify the effectiveness of the proposed method.