Abnormal crowd behavior detection using high-frequency and spatio-temporal features
Abnormal crowd behavior detection using high-frequency and spatio-temporal features
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DOI：
10.1007/s00138-011-0341-0
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发表时间：
2011-05
影响因子：
3.3
通讯作者：
Bo Wang-;Mao Ye;Xue Li;Fengjuan Zhao;Jian Ding
中科院分区：
文献类型：
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作者：
Bo Wang-;Mao Ye;Xue Li;Fengjuan Zhao;Jian Ding
人群异常行为检测是计算机视觉领域的一个重要研究课题。传统的方法首先从视频中提取局部时空立方体。然后用光流或梯度特征等描述长方体。遗憾的是，由于复杂的环境条件，如严重的遮挡、过度拥挤等，现有的算法不能有效地应用。本文提出了一种检测视频中异常人群行为的高频时空特征。它们是通过对与时间方向平行的长方体中的平面进行小波变换而得到的。高频信息表征了长方体的动态特性。将HFST特征应用于全局和局部异常人群行为检测。对于全局异常人群行为检测，采用潜在Dirichlet模型对正常场景进行建模。对于局部异常人群行为检测，采用具有竞争机制的多隐马尔可夫模型对正常场景进行建模。综合实验结果表明，该方法大大提高了检测速度。此外，考虑到误检率和漏检率，也取得了较好的准确率。
Abnormal crowd behavior detection is an important research issue in computer vision. The traditional methods first extract the local spatio-temporal cuboid from video. Then the cuboid is described by optical flow or gradient features, etc. Unfortunately, because of the complex environmental conditions, such as severe occlusion, over-crowding, etc., the existing algorithms cannot be efficiently applied. In this paper, we derive the high-frequency and spatio-temporal (HFST) features to detect the abnormal crowd behaviors in videos. They are obtained by applying the wavelet transform to the plane in the cuboid which is parallel to the time direction. The high-frequency information characterize the dynamic properties of the cuboid. The HFST features are applied to the both global and local abnormal crowd behavior detection. For the global abnormal crowd behavior detection, Latent Dirichlet allocation is used to model the normal scenes. For the local abnormal crowd behavior detection, Multiple Hidden Markov Models, with an competitive mechanism, is employed to model the normal scenes. The comprehensive experiment results show that the speed of detection has been greatly improved using our approach. Moreover, a good accuracy has been achieved considering the false positive and false negative detection rates.