A distributed solution for multi-object tracking and classification

This paper presents a distributed solution for multi-object tracking and classification. The state of objects is partially observed by a set of sensors organized in a network. The idea is to exchange partial data throughout the network and provide a complete information at each sensor level. The proposed solution involves a finite time average consensus where existing solutions are based on asymptotic consensus. The consensus algorithm intervenes in both distributed tracking and classification of multiple objects. It is firstly used to complete information about objects trajectories and secondly to complete beliefs concerning the classification. Simulation results show the relevance of the proposed solution.