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Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each one of them.Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another.
Multi-people tracking, Multi-camera, Visual surveillance, Probabilistic occupancy map, Dynamic Programming, Hidden Markov Model

P. Fua, J. Berclaz, F. Fleuret and R. Lengagne, "Multicamera People Tracking with a Probabilistic Occupancy Map," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 267-282, 2007.
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