Multi-view spatial integration and tracking with Bayesian networks

We present a novel method for the spatial integration of multiple views as a means for tracking point features in the presence of occlusion. The proposed technique employs a dynamic, multi-dimensional Bayesian network to combine information from multiple views. To achieve real-time performance, the system is implemented in a distributed fashion; the two-dimensional tracking for each view, as well as the spatial integration, occurs on a dedicated processor. We demonstrate the efficacy of the proposed spatial integration on the multi-view tracking of a person in a home environment. Our results show a considerable increase in the accuracy of tracking features throughout periods of occlusion.