Distributed sigma point information filters for target tracking in camera networks

Abstract

Distributed processing is a new paradigm to analyse the huge volume of video data in camera networks. This paper addresses the problem of distributed single target tracking considering false positives and missed detections. Target tracking is modelled as a dynamic state estimation problem with nonlinear process and measurement model. We propose to use the sigma point information filters combined with a consensus algorithm. Sigma point information filters are integrated with probabilistic data association filter to deal with false positives and missed detections. We use a distributed average consensus algorithm which converges in finite time. Unlike other related state of the art technique papers, we report results on real data and show the effectiveness of the proposed algorithm.

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

“…A centralized network needs a stable central node with excellent communication performance, which is not yet realistic in large-scale networks. Therefore, a large number of researchers are focusing on distributed sensor networks [6,7].…”