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Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

机译：基于目标的轨迹分析，用于智能监控中的异常行为检测

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摘要

In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent's trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.
机译：在典型的监视安装中，操作员必须不断监视大量视频源中的可疑行为。随着摄像机数量的增加，信息过载使手动监视变得越来越困难，这增加了其他令人困惑的因素，例如人的疲劳和无聊。智能的基于视觉的监视系统的目标是使监视的监视和事件检测组件自动化，仅在检测到异常行为或其他感兴趣的事件时才警告操作员。尽管用于基于轨迹的异常行为检测的大多数传统方法都依赖于低级轨迹特征（例如流矢量或控制点），但本文基于最近引入的方法，该方法利用了有意性的高级特征。将场景中的个体建模为故意的特工，并通过评估特工轨迹相对于已知空间目标的可显示性来检测异常行为。所提出的方法通过三种方式扩展了原始的基于目标的方法：首先，在训练阶段学习空间场景结构；第二，学习区域转换模型来描述空间区域之间的正常运动模式。第三，使用粒子滤波在概率框架中对进行中的轨迹进行分类。对三个已发布的第三方数据集的实验验证证明了该方法的有效性。

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  • 来源
    《Image and Vision Computing》 |2011年第4期|p.230-240|共11页
  • 作者

    Frederick Tung; John S. Zelek; David A. Clausi;

  • 作者单位

    Vision and Image Processing Lab, Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada, N2L 3G1;

    Vision and Image Processing Lab, Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada, N2L 3G1;

    Vision and Image Processing Lab, Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada, N2L 3G1;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Video surveillance; behaviour understanding; trajectory analysis; anomaly detection;

    机译：视频监控;行为理解;轨迹分析;异常检测;

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