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https://hdl.handle.net/2440/132993
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Type: | Journal article |
Title: | Distributed multi-object tracking under limited field of view sensors |
Author: | Nguyen, H.V. Rezatofighi, H. Vo, B.-N. Ranasinghe, D.C. |
Citation: | IEEE Transactions on Signal Processing, 2021; 69:5329-5344 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2021 |
ISSN: | 1053-587X 1941-0476 |
Statement of Responsibility: | Hoa Van Nguyen, Hamid Rezatofighi, Ba-Ngu Vo, Damith C. Ranasinghe |
Abstract: | We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm . To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel label consensus approach that reduces label inconsistency caused by objects’ movements from one node’s limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution’s real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios. |
Keywords: | Multi-sensor multi-object tracking; distributed multi-object tracking; label consistency; track consensus |
Rights: | © 2021 Crown Copyright |
DOI: | 10.1109/TSP.2021.3103125 |
Grant ID: | http://purl.org/au-research/grants/arc/DP160104662 http://purl.org/au-research/grants/arc/LP160101177 |
Published version: | https://ieeexplore.ieee.org/document/9513575/ |
Appears in Collections: | Electrical and Electronic Engineering publications |
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