NDLI: Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

Content Provider	IEEE Xplore Digital Library
Author	Bolei Zhou Xiaogang Wang Xiaoou Tang
Copyright Year	2012
Description	Author affiliation: Department of Information Engineering, The Chinese University of Hong Kong (Bolei Zhou; Xiaoou Tang) \|\| Department of Electronic Engineering, The Chinese University of Hong Kong (Xiaogang Wang)
Abstract	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 modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.
Starting Page	2871
Ending Page	2878
File Size	1841376
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6248013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Computational modeling Timing Dynamics Tracking Heuristic algorithms Force
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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