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Tracking applications based on distributed and embedded sensor networks are emerging today, both in the field of surveillance (airports, lab facilities, train stations, museums, public spots) and industrial vision (visual servoing, factory automation). Traditional centralized approaches offer several drawbacks, due to limited communication bandwidth, computational requirements and thus also limited spatial camera resolution and framerate. In this paper, we present a network-enabled Smart Camera for probabilistic tracking. It is capable of tracking objects adaptively in real-time and offers a very bandwidthconservative approach, as the whole computation is performed embedded in the Smart Camera, and only the tracking results are transmitted which are on a higher level of abstraction.