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This paper deals with the problem of tracking multiple targets in a distributed network of self-configuring pan-tilt-zoom cameras. We focus on applications where events unfold over a large geographic area and need to be analyzed by multiple overlapping and non-overlapping active cameras without a central unit accumulating and analyzing all the data. The overall goal is to keep track of all targets in the region of deployment of the cameras, while selectively focusing at a high resolution on some particular target features. To acquire all the targets at the desired resolutions while keeping the entire scene in view, we use cooperative network control ideas based on multi-player learning in games. For tracking the targets as they move through the area covered by the cameras, we propose a special application of the distributed estimation algorithm known as Kalman-Consensus filter through which each camera comes to a consensus with its neighboring cameras about the actual state of the target. This leads to a camera network topology that changes with time. Combining these ideas with single-view analysis, we have a completely distributed approach for multi-target tracking and camera network self-configuration. We show performance analysis results with real-life experiments on a network of 10 cameras.