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http://dx.doi.org/10.3837/tiis.2016.11.018

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)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.11, 2016 , pp. 5530-5546 More about this Journal
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.
Keywords
Human activity; Crowd surveillance; Bag-of-visual-words; CC-CTM;
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