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Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents

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Abstract

Collective behaviors characterize the intrinsic dynamics of the crowds. Automatically understanding collective crowd behaviors has important applications to video surveillance, traffic management and crowd control, while it is closely related to scientific fields such as statistical physics and biology. 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 from video sequences. From agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with initial and termination states reflecting the pedestrian’s belief of the starting point and the destination. The whole crowd is then modeled as a mixture of dynamic pedestrian-agents. Once the model parameters are learned from the trajectories extracted from videos, MDA can simulate the crowd behaviors. It can also infer the past behaviors and predict the future behaviors of pedestrians given their partially observed trajectories, and classify them different pedestrian behaviors. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on various video surveillance sequences.

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Notes

  1. Datasets, demo videos and related materials are available from http://mmlab.ie.cuhk.edu.hk/project/dynamicagent/

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Acknowledgments

This work is partially supported by the General Research Fund sponsored by the Research Grants Council of Hong Kong (Project No. CUHK417110, CUHK417011, and CUHK 429412).

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Bolei Zhou

  2. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Xiaoou Tang

  3. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Xiaogang Wang

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  1. Bolei Zhou
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  2. Xiaoou Tang
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  3. Xiaogang Wang
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Correspondence to Bolei Zhou.

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Communicated by M. Hebert.

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Zhou, B., Tang, X. & Wang, X. Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents. Int J Comput Vis 111, 50–68 (2015). https://doi.org/10.1007/s11263-014-0735-3

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