Integrated video object tracking with applications in trajectory-based event detection

  • Authors:

  • Hsu-Yung Cheng;Jenq-Neng Hwang

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan;Department of Electrical Engineering, University of Washington, 185 Stevens Way, Paul Allen Center, Seattle, WA, USA

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2011

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Abstract

This work presents an automated and integrated framework that robustly tracks multiple targets for video-based event detection applications. Integrating the advantages of adaptive particle sampling and mathematical tractability of Kalman filtering, the proposed tracking system achieves both high tracking accuracy and computational simplicity. Occlusion and segmentation error cases are analyzed and resolved by constructing measurement candidates via adaptive particle sampling and an enhanced version of probabilistic data association. Also, we integrate the initial occlusion handling module in the tracking system to backtrack and correct the object trajectories. The reliable tracking results can serve as the foundation for automatic event detection. We also demonstrate event detection by classifying the trajectories of the tracked objects from both traffic monitoring and human surveillance applications. The experimental results have shown that the proposed tracking mechanism can solve the occlusion and segmentation error problems effectively and the events can be detected with high accuracy.