Distributed Data Association in Smart Camera Networks Using Belief Propagation

One of the fundamental requirements for visual surveillance with smart camera networks is the correct association of camera's observations with the tracks of objects under tracking. Most of the current systems work in a centralized manner in that the observations on all cameras need to be transmitted to a central server where some data association algorithm is running. Recently some works have been shown for distributed data association based solely on appearance observation. However, how to perform distributed association inference using both appearance and spatio-temporal information is still unclear. In this article, we present a novel method for estimating the posterior distribution of the label of each observation, indicating which of the objects it comes from, based on belief propagation between neighboring cameras. We develop distributed forward and backward inference algorithms for online and offline application, respectively, and further extend the algorithms to the case of unreliable detection. We also incorporate the proposed inference algorithms into distributed EM framework to simultaneously solve the problem of data association and appearance model learning in a completely distributed manner. The proposed method is verified on artificial data and on real world observations collected by a camera networks in an office building.