The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
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.
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Citations
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Cited by
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction
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Self-citation
Metadata
- Authors
- Zhou, B., Wang, X., & Tang, X.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2871-2878
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2125095437
- Cited by
- 313
- References
- 24
- FWCI
- 34.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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