Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89559
Title: Learning behavior patterns from video for agent-based crowd modeling and simulation
Authors: Zhong, Jinghui
Cai, Wentong
Luo, Linbo
Zhao, Mingbi
Keywords: Crowd Modeling And Simulation
Agent-based Modeling
DRNTU::Engineering::Computer science and engineering
Issue Date: 2016
Source: Zhong, J., Cai, W., Luo, L., & Zhao, M. (2016). Learning behavior patterns from video for agent-based crowd modeling and simulation. Autonomous Agents and Multi-Agent Systems, 30(5), 990-1019. doi:10.1007/s10458-016-9334-8
Series/Report no.: Autonomous Agents and Multi-Agent Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/89559
http://hdl.handle.net/10220/47082
ISSN: 1387-2532
DOI: http://dx.doi.org/10.1007/s10458-016-9334-8
Rights: © The Author(s) (Published by Springer).
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
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