Abstract
We present a multi-camera system based on Bayesian modality fusion to track multiple people in an indoor environment A Bayesian network is used to combine multiple modalities for matching subjects between multiple camera views. Unlike other occlusion reasoning methods, we use multiple cameras to obtain continuous visual information of people in either or both cameras so that they can be tracked through interactions. Results demonstrate that the system can maintain people’s identities by using multiple cameras cooperatively.
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