Data Fusion by Belief Propagation for Multi-Camera Tracking
Wei Du, Justus Piater · 2006
Multi-camera tracking poses a data fusion problem that integrates image measurements from different cameras. A novel approach to tracking using multiple cameras is proposed that combines particle filters and belief propagation in a unified framework. In each view, a target is tracked by a dedicated particle-filter-based local tracker. The trackers in different views collaborate via belief propagation so that a local tracker operating in one view is able to take advantage of additional information from other views. The message passing mechanism in belief propagation guarantees that wrong information is not propagated across views, thus avoiding a common problem in multi-camera tracking. Target states in each view and in 3D are inferred based on the multi-view image measurements by a set of particle filters, and a sequential belief propagation algorithm implements collaboration between the view-specific particle filters. We demonstrate the effectiveness of our approach on sequences of video surveillance and soccer games