Crowd Activity Classification Using Category Constrained Correlated Topic Model

• Xianping Huang,
• Wanliang Wang,
• Guojiang Shen,
• Xiaoqing Feng,
• Xiangjie Kong,

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

• Human activity
• Crowd surveillance
• Bag-of-visual-words
• CC-CTM

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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