Distributed Multi-Object Tracking Under Limited Field of View Sensors

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 t...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2021, Vol.69, p.5329-5344
Main Authors: Nguyen, Hoa Van, Rezatofighi, Hamid, Vo, Ba-Ngu, Ranasinghe, Damith C.
Format: Article
Language:English
Subjects:
Algorithms
Australia
Bandwidth
Computer networks
distributed multi-object tracking
Field of view
label consistency
Multi-sensor multi-object tracking
Multiple target tracking
Nodes
Object recognition
Sensor fusion
Sensors
Signal processing algorithms
track consensus
Tracking errors
Trajectory
Wireless sensor networks
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2021.3103125