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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Robust Multi-person Tracking for Real-Time Intelligent Video Surveillance

  • Received : 2014.05.22
  • Accepted : 2015.01.17
  • Published : 2015.05.01
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Abstract

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

Keywords

References

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