Learning Collective Crowd Behaviors with Dynamic Pedestrian-Agents
Authored by Bolei Zhou, Xiaoou Tang, Xiaogang Wang
Date Published: 2015
DOI: 10.1007/s11263-014-0735-3
Sponsors:
Council of Hong Kong
Platforms:
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Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
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.
Tags
models
pattern
Visual surveillance
Video
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