Learning behavior patterns from video for agent-based crowd modeling and simulation


This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.

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  1. 1.

    The source code, training data and simulation videos can be downloaded from: http://crowds.sce.ntu.edu.sg/resource.html.

  2. 2.

    The original video and extracted trajectories are downloaded from http://www.ee.cuhk.edu.hk/~xgwang/grandcentral.html.

  3. 3.

    The video and extracted trajectories are downloaded from http://www.vision.ee.ethz.ch/datasets/index.en.html.


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The research reported in this paper is financially supported by the Tier 1 Academic Research Fund (AcRF) under Project Number RG23/14. Linbo Luo is supported by National Natural Science Foundation of China (Grant No. 61502370), China 111 Project (No. B16037) and Fundamental Research Funds for the Central Universities (Grant No. JB150305).

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Correspondence to Jinghui Zhong.

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Zhong, J., Cai, W., Luo, L. et al. Learning behavior patterns from video for agent-based crowd modeling and simulation. Auton Agent Multi-Agent Syst 30, 990–1019 (2016). https://doi.org/10.1007/s10458-016-9334-8

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  • Agent-based modeling
  • Crowd modeling and simulation
  • Data-driven modeling
  • Behavior pattern