Robust recognition of specific human behaviors in crowded surveillance video sequences

  • Authors:

  • Masaki Takahashi;Mahito Fujii;Masahiro Shibata;Shin'ichi Satoh

  • Affiliations:

  • Human & Information Science Research Devision, Science and Technology Research Laboratories, Japan Broadcasting Corporation, Tokyo, Japan and Department of Informatics, The Graduate University ...;Human & Information Science Research Devision, Science and Technology Research Laboratories, Japan Broadcasting Corporation, Tokyo, Japan;Human & Information Science Research Devision, Science and Technology Research Laboratories, Japan Broadcasting Corporation, Tokyo, Japan;Department of Informatics, The Graduate University for Advanced Studies, Kanagawa, Japan and Multimedia Information Research Devision, National Institute of Informatics, Tokyo, Japan

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
  • Year:
  • 2010

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Abstract

We describe a method that can detect specific human behaviors even in crowded surveillance video scenes. Our developed system recognizes specific behaviors based on the trajectories created by detecting and tracking people in a video. It detects people using an HOG descriptor and SVM classifier, and it tracks the regions by calculating the two-dimensional color histograms. Our system identifies several specific human behaviors, such as running and meeting, by analyzing the similarities to the reference trajectory of each behavior. Verification techniques such as backward tracking and calculating optical flows contributed to robust recognition. Comparative experiments showed that our system could track people more robustly than a baseline tracking algorithm even in crowded scenes. Our system precisely identified specific behaviors and achieved first place for detecting running people in the TRECVID 2009 Surveillance Event Detection Task.