Non-overlapping distributed tracking using particle filter
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Tracking people or objects across multiple cameras is a challenging research area in visual computing especially when these cameras have non-overlapping field-of-views. The important task is to associate a target of interest with its previous appearances across time and space within the camera network. In this paper, we propose a unified tracking framework using particle filter to efficiently switch between track prediction (to deal with non-overlapping region tracking) and visual tracking. The particle filter tracking system uses a map to provide the possible trajectory information of the target as it moves within the non-overlapping regions. We implemented and tested this tracking approach in an in-house multiple cameras system. Promising results were obtained which suggested the feasibility of such an approach.
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