Field of view

Pattern recognition (psychology)

Pedagogy

Pure mathematics

Consistency (knowledge bases)

Computer network

Mathematics

Wireless

Structural engineering

Algorithm

Cognitive radio

Engineering

Artificial intelligence

Psychology

Wireless sensor network

Set (abstract data type)

Tracking (education)

Programming language

Video tracking

Object detection

Code (set theory)

Sensor fusion

Computer science

Fusion center

Object (grammar)

Distributed object

Field (mathematics)

Computer vision

State (computer science)

Node (physics)

Telecommunications

IEEE Transactions on Signal Processing

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 \textit{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. We also release source code at https://github.com/AdelaideAuto-IDLab/Distributed-limitedFoV-MOT for our fusion method to foster developments in DMOT algorithms.

Distributed Multi-Object Tracking Under Limited Field of View Sensors