显著性光流直方图字典表示的群体异常事件检测

Dictionary Representation for Group Anomaly Event Detection Based on the Histograms of Salience Optical Flow

在视频监控系统已被广泛应用的今天,基于监控视频的群体异常事件检测已成为保障社会安全的迫切需要,越来越受到人们的重视。该文基于这一现状,提出了一个新的群体异常事件检测方案,实现对监控视频自动高效的检测。在特征提取方面,提出了显著性光流直方图特征描述符,并利用该特征描述符构建字典;在字典优化方面,提出了基于聚类的多字典组合学习框架,将原始的大字典分为多个子字典;最后,对于测试样本,找出最适合的子字典并计算测试样本在该子字典下的重建误差,即可判断测试样本是否异常。在两个数据集上的实验表明,与其他方法相比,该文提出的方法对拥挤场景下监控视频中的群体异常事件检测取得了较好的检测性能。

Nowadays,video surveillance system has been widely used. People paid more and more attentions on the group anomaly detection of video surveillance,which has become an urgent need to protect public security. In this paper,a new scheme for group anomaly event detection is proposed,in which it is efficient to automatically detect abnormal events in the video surveillance. For the feature extraction,a new feature descriptor called Histograms of Salience Optical Flow（ HSOF）is proposed,which is used for the dictionary construction. Then a cluster-based multi-dictionary learning framework is proposed for dictionary optimization,which separates the original large dictionary into some sub-dictionaries. Finally,for the test samples,we can find the most suitable sub-dictionary and calculate the reconstruction error to determine whether the sample is normal or not. Compared with other methods for the group anomaly events detection in crowd scenes,experiments on two datasets show that our proposed method obtains better results.