Distributed multi target tracking in camera networks using sigma point information filters

Multiple target tracking is an important problem in analysing video data in camera networks. Distributed processing is a promising scheme to deal with huge volume of video data in camera networks. This paper addresses the problem of distributed multiple target tracking in camera networks. Each camera shares measurements with its immediate neighbours and performs inter-camera measurement-to-measurement association in distributed manner. The measurements are assigned to the targets using the nearest neighbourhood principle. To update the target state we use probabilistic data association with sigma point information filters. This filter is integrated with a consensus algorithm to develop distributed multi target tracking algorithm. We evaluated the proposed algorithm on various real world datasets and show that our algorithm outperforms the other related state-of-art distributed algorithms.