计算机工程2017，Vol.43Issue(10)：228-233,6.DOI:10.3969/j.issn.1000-3428.2017.10.038
利用综合光流直方图的人群异常行为检测
Abnormal Crowd Behavior Detection Using Synthesized Optical Flow Histogram
摘要
Abstract
To deal with the issue of intelligent video surveillance in public places,a novel method detecting abnormal crowd behavior from videos is proposed.The moving foreground is extracted from the video by using the Gaussian Mixture Model(GMM).Feature points are extracted from foreground regions by an equidistant sampling method.In the stage of crowd feature extraction,an optical flow feature extraction method is presented,where the Lucas-Kanade method is used to calculate the optical flow field.Crowd features are constructed by synthesizing three kinds of histograms including orientation,magnitude and acceleration of the optical flow.The Support Vector Machine (SVM) is applied to train and predict the feature data from the total histogram.Experimental results show that the proposed method can effectively detect abnormal crowd behaviors in real time compared with the methods based on social force model and pure histogram of optical flow.The detection rate in the UMN dataset is greater than 97％.关键词
异常行为检测/Lucas-Kanade光流/特征点提取/运动矢量/支持向量机Key words
abnormal behavior detection/Lucas-Kanade optical flow/feature point extraction/motion vector/Support Vector Machine (SVM)分类
信息技术与安全科学引用本文复制引用
熊饶饶,胡学敏,陈龙,周慧子..利用综合光流直方图的人群异常行为检测[J].计算机工程,2017,43(10):228-233,6.基金项目
国家自然科学基金青年科学基金(41401525) （41401525）
湖北省大学生创新创业训练计划项目(201510512041). （201510512041）
