Non-overlapping Distributed Tracking using Particle Filter

Authors:
Wilson Leoputra;Tele Tan;Fee Lee Lim
Affiliations:
Curtin University of Technology, Perth, Western Australia;Curtin University of Technology, Perth, Western Australia;Curtin University of Technology, Perth, Western Australia
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

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Abstract

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.