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Keywords = unresolvable group target tracking

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38 pages, 4236 KB  
Article
Variational Gaussian Mixture Model for Tracking Multiple Extended Targets or Unresolvable Group Targets in Closely Spaced Scenarios
by Yuanhao Cheng, Yunhe Cao, Tat-Soon Yeo, Yulin Zhang and Jie Fu
Remote Sens. 2025, 17(22), 3696; https://doi.org/10.3390/rs17223696 - 12 Nov 2025
Viewed by 503
Abstract
Many multi-target tracking applications (e.g., tracking multiple targets with LiDAR or millimeter-wave radar) are challenged by closely spaced targets. In this work, we propose a method for the tracking of multiple extended targets or unresolvable group targets in such scenarios. The approach builds [...] Read more.
Many multi-target tracking applications (e.g., tracking multiple targets with LiDAR or millimeter-wave radar) are challenged by closely spaced targets. In this work, we propose a method for the tracking of multiple extended targets or unresolvable group targets in such scenarios. The approach builds on the cardinality probability hypothesis density (CPHD) filtering framework for computational efficiency, models the target’s extent with the multiplicative error model (MEM), and uses variational Gaussian mixture model (VGMM)-derived responsibilities to drive probabilistic data association (PDA) measurement updates. This effectively mitigates state fusion between closely spaced targets and yields more accurate state estimation. In experiments on diverse simulated and real datasets, the proposed method consistently outperforms existing approaches, achieving the lowest localization, shape estimation, and cardinality estimation errors while maintaining an acceptable runtime and scalability. Full article
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20 pages, 2363 KB  
Article
Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections
by Andinet Hunde
Sensors 2022, 22(21), 8415; https://doi.org/10.3390/s22218415 - 2 Nov 2022
Cited by 1 | Viewed by 2092
Abstract
This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem—on the first layer, a multi-target tracking problem based on multiple detections is [...] Read more.
This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem—on the first layer, a multi-target tracking problem based on multiple detections is distinguished in the measurement space, and on the second (top) layer, group target tracking with birth and death as well as merging and splitting of group target tracks as they evolve in a dynamic scene is represented. This configuration enhances the multi-target tracking performance in situations including but not limited to target initialization(birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group tracking exposes complex individual target interactions to help in situation assessment which is challenging to capture otherwise. Full article
(This article belongs to the Special Issue Multimodal Sensing for Vehicle Detection and Tracking)
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