Multi-camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration

  • Wei Du
  • Justus Piater
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)


This paper presents a novel approach to tracking people in multiple cameras. A target is tracked not only in each camera but also in the ground plane by individual particle filters. These particle filters collaborate in two different ways. First, the particle filters in each camera pass messages to those in the ground plane where the multi-camera information is integrated by intersecting the targets’ principal axes. This largely relaxes the dependence on precise foot positions when mapping targets from images to the ground plane using homographies. Secondly, the fusion results in the ground plane are then incorporated by each camera as boosted proposal functions. A mixture proposal function is composed for each tracker in a camera by combining an independent transition kernel and the boosted proposal function. Experiments show that our approach achieves more reliable results using less computational resources than conventional methods.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Wei Du
    • 1
  • Justus Piater
    • 1
  1. 1.University of Liège, Department of Electrical Engineering and Computer Science, Montefiore Institute, B28, Sart Tilman Campus, B-4000 LiègeBelgium

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