KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Crowd Activity Classification Using Category Constrained Correlated Topic Model

  • Huang, Xianping (College of Computer Science and Technology, Zhejiang University of Technology) ;
  • Wang, Wanliang (College of Computer Science and Technology, Zhejiang University of Technology) ;
  • Shen, Guojiang (College of Computer Science and Technology, Zhejiang University of Technology) ;
  • Feng, Xiaoqing (College of Information, Zhejiang University of Finance &Economics) ;
  • Kong, Xiangjie (School of Software, Dalian University of Technology)
  • Received : 2015.04.10
  • Accepted : 2016.09.22
  • Published : 2016.11.30
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Abstract

Automatic analysis and understanding of human activities is a challenging task in computer vision, especially for the surveillance scenarios which typically contains crowds, complex motions and occlusions. To address these issues, a Bag-of-words representation of videos is developed by leveraging information including crowd positions, motion directions and velocities. We infer the crowd activity in a motion field using Category Constrained Correlated Topic Model (CC-CTM) with latent topics. We represent each video by a mixture of learned motion patterns, and predict the associated activity by training a SVM classifier. The experiment dataset we constructed are from Crowd_PETS09 bench dataset and UCF_Crowds dataset, including 2000 documents. Experimental results demonstrate that accuracy reaches 90%, and the proposed approach outperforms the state-of-the-arts by a large margin.

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References

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