Spatial Clutter Measurement Density Estimation with the Clutter Probability for Improving Multi-Target Tracking Performance in Cluttered Environments

2018 ◽  
Author(s):  
Seung Hyo Park ◽  
Yifan Xie ◽  
Du Hee Hant ◽  
Taek Lyul Song
Keyword(s):  
Target Tracking ◽  
Density Estimation ◽  
Tracking Performance ◽  
Cluttered Environments ◽  
Multi Target Tracking

scholarly journals Adaptive Estimation of Spatial Clutter Measurement Density Using Clutter Measurement Probability for Enhanced Multi-Target Tracking

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 114
Author(s):  
Seung Hyo Park ◽  
Sa Yong Chong ◽  
Hyung June Kim ◽  
Taek Lyul Song
Keyword(s):  
Monte Carlo Simulation ◽  
Target Tracking ◽  
Density Estimation ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Radar Data ◽  
Tracking Performance ◽  
Density Estimator ◽  
Density Estimates ◽  
Multi Target Tracking

The point detections obtained from radars or sonars in surveillance environments include clutter measurements, as well as target measurements. Target tracking with these data requires data association, which distinguishes the detections from targets and clutter. Various algorithms have been proposed for clutter measurement density estimation to achieve accurate and robust target tracking with the point detections. Among them, the spatial clutter measurement density estimator (SCMDE) computes the sparsity of clutter measurement, which is the reciprocal of the clutter measurement density. The SCMDE considers all adjacent measurements only as clutter, so the estimated clutter measurement density is biased for multi-target tracking applications, which may result in degraded target tracking performance. Through the study in this paper, a major source of tracking performance degradation with the existing SCMDE for multi-target tracking is analyzed, and the use of the clutter measurement probability is proposed as a remedy. It is also found that the expansion of the volume of the hyper-sphere for each sparsity order reduces the bias of clutter measurement density estimates. Based on the analysis, we propose a new adaptive clutter measurement density estimation method called SCMDE for multi-target tracking (MTT-SCMDE). The proposed method is applied to multi-target tracking, and the improvement of multi-target tracking performance is shown by a series of Monte Carlo simulation runs and a real radar data test. The clutter measurement density estimation performance and target tracking performance are also analyzed for various sparsity orders.

scholarly journals Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4315 ◽  
Author(s):  
Meiqin Liu ◽  
Tianyi Huai ◽  
Ronghao Zheng ◽  
Senlin Zhang
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Gaussian Mixture ◽  
Tracking Performance ◽  
Probability Hypothesis Density ◽  
Single Target ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Single Target Tracking ◽  
Better Than

In this paper, we study the issue of out-of-sequence measurement (OOSM) in a multi-target scenario to improve tracking performance. The OOSM is very common in tracking systems, and it would result in performance degradation if we used it inappropriately. Thus, OOSM should be fully utilized as far as possible. To improve the performance of the tracking system and use OOSM sufficiently, firstly, the problem of OOSM is formulated. Then the classical B1 algorithm for OOSM problem of single target tracking is given. Next, the random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) is introduced. Consequently, we derived the equation for re-updating of posterior intensity with OOSM. Implementation of GM-PHD using OOSM is also given. Finally, several simulations are given, and results show that tracking performance of GM-PHD using OOSM is better than GM-PHD using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness of our proposed algorithm.

Performance Evaluation Based on Information Theory for Multi-Target Tracking Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2341-2344
Author(s):  
Shu Rong Tian ◽  
Xiao Shu Sun ◽  
Dan Liu
Keyword(s):  
Information Theory ◽  
Performance Evaluation ◽  
Target Tracking ◽  
Ground Truth ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Actual State ◽  
Target Number ◽  
Multi Target Tracking

This paper is concerned with the performance evaluation of algorithm of multi-target and target types tracking. Performance evaluation is based on information theory, Kullback-Leibler measure is used to discriminate information provided by algorithm. Through simulations, algorithm of multi-target tracking was evaluated in term of information (localization, classification, and target number components) the algorithm provide about the actual state of ground truth.

scholarly journals Extended smoothing joint data association for multi‐target tracking in cluttered environments

2020 ◽  
Vol 14 (4) ◽  
pp. 564-571 ◽  
Author(s):  
Sufyan Ali Memon ◽  
Myunggun Kim ◽  
Minho Shin ◽  
Jawaid Daudpoto ◽  
Dur Muhammad Pathan ◽  
...  
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Cluttered Environments ◽  
Multi Target Tracking

scholarly journals Combinatorial Optimization for Multi-Target Tracking

2020 ◽  
Author(s):  
Mohib Ullah ◽  
Maqsood Mahmud ◽  
Habib Ullah ◽  
Kashif Ahmad ◽  
Ali Shariq Imran ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Target Tracking ◽  
Network Flow ◽  
Graph Matching ◽  
The Body ◽  
Tracking Performance ◽  
Whole Body ◽  
Multi Target Tracking ◽  
Velocity Motion ◽  
Spatial Locations

In tracking-by-detection paradigm for multi-target tracking, target association is modeled as an optimization problem that is usually solved through network flow formulation. In this paper, we proposed combinatorial optimization formulation and used a bipartite graph matching for associating the targets in the consecutive frames. Usually, the target of interest is represented in a bounding box and track the whole box as a single entity. However, in the case of humans, the body goes through complex articulation and occlusion that severely deteriorate the tracking performance. To partially tackle the problem of occlusion, we argue that tracking the rigid body organ could lead to better tracking performance compared to the whole body tracking. Based on this assumption, we generated the target hypothesis of only the spatial locations of person’s heads in every frame. After the localization of head location, a constant velocity motion model is used for the temporal evolution of the targets in the visual scene. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.

Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter

2019 ◽  
Vol 2019 (13) ◽  
pp. 127-1-127-7
Author(s):  
Benjamin J. Foster ◽  
Dong Hye Ye ◽  
Charles A. Bouman
Keyword(s):  
Target Tracking ◽  
Vision Sensor ◽  
Multi Target Tracking ◽  
Event Based
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