Crowd Event Perception Based on Spatio-temporal Viscous Fluid Field

Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, Sha Wei
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引用次数: 17

Abstract

Over the past decades, a wide attention has been paid to crowd control and management in intelligent video surveillance area. In this paper, the authors propose a novel spatiotemporal viscous fluid field to recognize large-scale crowd event with respect to both appearance and driven factor of crowd behavior. Firstly, a spatiotemporal variation matrix is proposed to exploit motion property of a crowd. In particular, the paper exploits characteristics of the matrix with eigenvalue decomposition algorithm and constructs an abstract fluid field to model the crowd motion pattern, which is denoted by spatiotemporal fluid field. Secondly, the paper proposes a spatiotemporal force field to exploit the interaction force between the pedestrians. Furthermore, the fluid and force field constructs a spatiotemporal viscous fluid field. Thirdly, after generating feature with bag of word model, the authors utilize latent Dirichlet allocation model to recognize crowd behavior. The experiments on PETS2009 and UMN datasets show that the proposed method has a better performance for large-scale crowd behavior perception in both robustness and effectiveness comparing with the conventional methods.
基于时空粘性流场的人群事件感知
在过去的几十年里,智能视频监控领域的人群控制与管理受到了广泛的关注。在本文中,作者提出了一种新的时空粘性流体场,从人群行为的外观和驱动因素两方面对大规模人群事件进行识别。首先,提出了利用人群运动特性的时空变化矩阵;特别地,本文利用特征值分解算法利用矩阵的特性,构建了一个抽象的流场来模拟人群的运动模式,用时空流场表示。其次,本文提出了一个时空力场来开发行人之间的相互作用力。此外,流体和力场构建了一个时空粘性流体场。第三,在用bag of word模型生成特征后,利用latent Dirichlet分配模型对人群行为进行识别。在PETS2009和UMN数据集上的实验表明,与传统方法相比,该方法在鲁棒性和有效性方面都具有更好的大规模人群行为感知性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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