Research on Abnormal Event Detection in Video Surveillance Based on Displacement of Feature Point and GLCM Texture Features

摘要：

　　The current anomaly detection algorithm uses more than a single feature to detect that causes trouble about the scene feature description and has the average accuracy of about 92%.This paper presented a new video anomaly detection algorithm using displacement feature and texture feature.The algorithm used pyramid LK optical flow method combined with Harris corner to extract the average displacement feature of moving target.GLCM algorithm and Fast Fourier Transform were introduced to get the texture feature of video images.Finally through SVM algorithms for two kinds of features classification training, the highest accuracy could reach 97.65%.Experiments show that the new algorithm has a higher accuracy and more practical.

关键词： Anomaly detection Pyramid LK optical flow Displacement GLCM Texture feature

作者: Yue WANG Xue-jun ZHANG Jin-wen DENG Mu-jun LIU

作者单位: School of Computer, Electronics and Information, Guangxi University, China;Guangxi Key Laboratory of Multimedia Communications and Network Technology,Guangxi University, Nanning 530004, China

会议类型: 国际会议

会议名称: 2018 International Conference on Physics, Computing and Mathematical Modeling(PCMM2018)(2018年物理计算和数学建模国际学术会议)

会议地点: 上海

会议语种:英文

页码: 1-6

在线出版日期: 2018-04-15（万方平台首次上网日期，不代表论文的发表时间）