Distributed consensus based IPDAF for tracking in vision networks

In this paper consensus based algorithms for distributed target tracking in large scale camera networks are discussed and a new adaptive algorithm is proposed. Camera networks are typically characterized by sparse communication and coverage topologies, as well as by the presence of multiple targets and clutter. The proposed algorithm (IPDA-ACF) is a result of the introduction of the probabilities of target perceivability and target existence in the basic distributed consensus based tracking algorithm (ACF). The distributed adaptation scheme for information fusion allows obtaining robustness in the cases of high level clutter and occulted targets, together with high level agreement between the nodes. A comparison with analogous methods derived from the Kalman Consensus Filter (KCF) and the Information Consensus Filter (ICF) shows that the proposed method achieves better performance, along with reduced communication requirements.