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Displaying 1-4 out of 4 total
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
Found in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Bolei Zhou, Xiaogang Wang, Xiaoou Tang
Issue Date:June 2012
pp. 2871-2878
In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeli...
 
Random field topic model for semantic region analysis in crowded scenes from tracklets
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Bolei Zhou, Xiaogang Wang, Xiaoou Tang
Issue Date:June 2011
pp. 3441-3448
In this paper, a Random Field Topic (RFT) model is proposed for semantic region analysis from motions of objects in crowded scenes. Different from existing approaches of learning semantic regions either from optical flows or from complete trajectories, our...
 
Measuring Crowd Collectiveness
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Bolei Zhou,Xiaoou Tang,Hepeng Zhang,Xiaogang Wang
Issue Date:August 2014
pp. 1586-1599
Collective motions of crowds are common in nature and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union, is a fundamental and universal measurement...
 
Measuring Crowd Collectiveness
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Bolei Zhou,Xiaoou Tang,Xiaogang Wang
Issue Date:June 2013
pp. 3049-3056
Collective motions are common in crowd systems and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union in collective motion, is a fundamental and uni...
 
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