• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Crowd Activity Classification Using Category Constrained Correlated Topic Model

Vol. 10, No. 11, November 29, 2016
10.3837/tiis.2016.11.018, Download Paper (Free):

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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Cite this article

[IEEE Style]
X. Huang, W. Wang, G. Shen, X. Feng and X. Kong, "Crowd Activity Classification Using Category Constrained Correlated Topic Model," KSII Transactions on Internet and Information Systems, vol. 10, no. 11, pp. 5530-5546, 2016. DOI: 10.3837/tiis.2016.11.018.

[ACM Style]
Xianping Huang, Wanliang Wang, Guojiang Shen, Xiaoqing Feng, and Xiangjie Kong. 2016. Crowd Activity Classification Using Category Constrained Correlated Topic Model. KSII Transactions on Internet and Information Systems, 10, 11, (2016), 5530-5546. DOI: 10.3837/tiis.2016.11.018.