结合全局与局部视频表示的视频异常检测算法  

Video Anomaly Detection Algorithm Combining Global and Local Video Representation

作　　者：胡正平[1,2] 赵梦瑶 辛丙一 HU Zhengping;ZHAO Mengyao;XIN Bingyi(School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004;Key Laboratory of Information Transmission and Signal Processing of Hebei Province,Yanshan University,Qinhuangdao 066004)

机构地区：[1]燕山大学信息科学与工程学院,秦皇岛066004 [2]燕山大学河北省信息传输与信号处理重点实验室,秦皇岛066004

出　　处：《模式识别与人工智能》2020年第2期133-140,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金面上项目(No.61771420);河北省自然科学基金项目(No.F2016203422)资助。

摘　　要：针对视频异常行为检测问题,提出结合全局与局部视频表示的视频异常检测算法.首先将输入视频连续多帧划分为视频块.再按空间位置将视频块划分为互不重叠的时空立方体,利用时空立方体运动特征构建基于空间位置的全局时空网格位置支持向量数据描述模型(SVDD).然后针对视频运动目标,提取局部纹理运动特征,采用SVDD获得围绕目标特征的超球体边界,构建运动目标正常行为模型.最后组合两部分以实现更全面的检测.公共数据集上的实验验证文中算法的有效性.Aiming at the problem of video anomaly detection,a video anomaly detection algorithm combining global and local video representation is proposed.Firstly,the input video continuous multi-frames are divided into video blocks.The video blocks are divided into non-overlapping space-time cubes according to the spatial position.The global spatiotemporal grid position support vector data description(SVDD)model based on spatial position is constructed using the space-time cubes motion features.Then,the local texture motion features are extracted for the moving targets of videos.SVDD algorithm is utilized to obtain the hypersphere boundary around the target features,and the normal behavior model of the moving targets is constructed.Finally,the two parts are combined to conduct more comprehensive detection.Experiments on public datasets verify the effectiveness of the proposed algorithm.

关 键 词：模式识别 异常行为检测 时空运动特征 局部纹理运动特征 支持向量数据描述模型(SVDD) 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]