Stochastic Adaptive Tracking In A Camera Network
We present a novel stochastic, adaptive strategy for tracking multiple people in a large network of video cameras. Similarities between features (appearance and biometrics) observed at different cameras are continuously adapted and the stochastically optimal path for each person computed. The following are the major contributions of the proposed approach. First, we consider situations where the feature similarities are uncertain and treat them as random variables. We show how the distributions of these random variables can be learned and how to compute the tracks in a stochastically optimal manner. Second, we consider the possibility of long-term interdependence of the features over space and time. This allows us to adoptively evolve the feature correspondences by observing the system performance over a time window, and correct for errors in the similarity computations. Third, we show that the above two conditions can be addressed by treating the issue of tracking in a camera network as an optimization problem in a stochastic adaptive system. We show results on data collected by a large camera network. The proposed approach is particularly suitable for distributed processing over the entire network.