This dissertation addresses the challenges in large scale deployment of wide-area camera networks and automated analysis of resulting big data. Analysis of such data is limited due to communication bottlenecks and low computational power at individual nodes. Specific focus is on distributed tracking and search/retrieval.

For object tracking in overlapping camera views, we propose a strategy for inducing priors on the scene specific information and explicitly modeling object appearance. Contextual information such as known trajectories and entry/exit points will be leveraged as scene specific priors. A novel probabilistic multiple camera tracking algorithm with a distributed loss function for incorporating scene priors is proposed, which leads to a significant improvement in the overall tracking accuracy. The proposed algorithm is validated with extensive experimentation in challenging camera network data, and is found to compare favorably with state of the art trackers. For non-overlapping views, a novel graph based model is proposed to represent spatial-temporal relationships between objects for search and retrieval tasks. A graph ranking strategy is used to order the items based on similarity with an emphasis on diversity. Extensive experimental results on a ten camera network are presented. The proposed person re-identification methodology is compared with the state-of-the-art algorithms in benchmark datasets.