Decentralized discovery of camera network topology

One of the primary uses of camera networks is the observation and tracking of objects within some domain. Substantial research has gone into tracking objects within single and multiple views. However, few such approaches scale to large numbers of sensors, and those that do require an understanding of the network topology. Camera network topology models camera adjacency in the context of tracking: when an object/entity leaves one camera, which cameras could it appear at next? This paper presents a decentralized approach for estimating a camera networkpsilas topology based on sequential Bayesian estimation using a modified multinomial distribution. Central to this method is an information-theoretic appearance model for observation weighting. The distributed nature of the approach utilizes all of the sensors as processing agents in collectively recovering the network topology. Experimental results are presented using camera networks varying in size from 10-100 nodes.