Robust Visual Tracking for Multiple Targets

  • Yizheng Cai
  • Nando de Freitas
  • James J. Little
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)


We address the problem of robust multi-target tracking within the application of hockey player tracking. The particle filter technique is adopted and modified to fit into the multi-target tracking framework. A rectification technique is employed to find the correspondence between the video frame coordinates and the standard hockey rink coordinates so that the system can compensate for camera motion and improve the dynamics of the players. A global nearest neighbor data association algorithm is introduced to assign boosting detections to the existing tracks for the proposal distribution in particle filters. The mean-shift algorithm is embedded into the particle filter framework to stabilize the trajectories of the targets for robust tracking during mutual occlusion. Experimental results show that our system is able to automatically and robustly track a variable number of targets and correctly maintain their identities regardless of background clutter, camera motion and frequent mutual occlusion between targets.


Video Frame Particle Filter Camera Motion Tracking Result Proposal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yizheng Cai
    • 1
  • Nando de Freitas
    • 1
  • James J. Little
    • 1
  1. 1.University of British ColumbiaVancouverCanada

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