Learning behavior patterns from video for agent-based crowd modeling and simulation

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• 作者：Jinghui Zhong ; Wentong Cai ; Linbo Luo…
• 刊名：Autonomous Agents and Multi-Agent Systems
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：30
• 期：5
• 页码：990-1019
• 全文大小：1,742 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Systems Organization and Communication Networks
  Computing Methodologies
  Software Engineering, Programming and Operating Systems
  User Interfaces and Human Computer Interaction
  
• 出版者：Springer Netherlands
• ISSN：1573-7454
• 卷排序：30

文摘

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.KeywordsAgent-based modelingCrowd modeling and simulationData-driven modelingBehavior pattern