Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents
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AbstractCollective 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.
All Author(s) ListZhou BL, Tang XO, Wang XG
Journal nameInternational Journal of Computer Vision
Detailed descriptionVol. 111
Volume Number111
Issue Number1
Pages50 - 68
LanguagesEnglish-United Kingdom
KeywordsCrowd behavior analysis; Motion analysis; Video surveillance
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-25-11 at 02:07