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We address the problem of tracking multiple people in a network of nonoverlapping cameras. This introduces certain challenges that are unique to this particular application scenario, in addition to existing challenges in tracking like pose and illumination variations, occlusion, clutter and sensor noise. For this purpose, we propose a novel multi-objective optimization framework by combining short term feature correspondences across the cameras with long-term feature dependency models. The overall solution strategy involves adapting the similarities between features observed at different cameras based on the long-term models and finding the stochastically optimal path for each person. For modeling the long-term interdependence of the features over space and time, we propose a novel method based on discriminant analysis models. The entire process allows us to adaptively evolve the feature correspondences by observing the system performance over a time window, and correct for errors in the similarity estimations. We show results on data collected by two large camera networks. These experiments prove that incorporation of the long-term models enable us to hold tracks of objects over extended periods of time, including situations where there are large ldquoblindrdquo areas. The proposed approach is implemented by distributing the processing over the entire network.