We present a multi-camera system based on Bayesian modality fusion to track multiple people in an indoor environment. Bayesian

We describe a system for tracking multiple people with multiple cameras based on fusion of multiple cues. Face trackers are used to self-calibrate our system. Epipolar geometry and landmarks are employed to disambiguate the tracking problem. The correlation of visual information between different cameras is learnt using Support Vector Regression and Hierarchical Principal Component Analysis to estimate the subject appearance across cameras. The joint features of subjects extracted from multiple cameras are tracked and used as a model to re-track people once the subjects are lost tracking in the system. Results demonstrate that our system can deal with the occlusion.

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