Abnormal event detection via covariance matrix for optical flow based feature
Wang, Tian1; Qiao, Meina1; Zhu, Aichun2; Niu, Yida1; Li, Ce3; Snoussi, Hichem4
2018-07
发表期刊MULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
卷号77期号:13页码:17375-17395
摘要Abnormal event detection is one of the most important objectives in security surveillance for public scenes. In this paper, a new high-performance algorithm based on spatio-temporal motion information is proposed to detect global abnormal events from the video stream as well as the local abnormal event. We firstly propose a feature descriptor to represent the movement by adopting the covariance matrix coding optical flow and the corresponding partial derivatives of multiple connective frames or the patches of the frames. The covariance matrix of multi-RoI (region of interest) which consists of frames or patches can represent the movement in high accuracy. For public surveillance video, the normal samples are abundant while there are few abnormal samples. Thus the one-class classification method is suitable for handling this problem inherently. The nonlinear one-class support vector machine based on a proposed kernel for Lie group element is applied to detect abnormal events by merely training the normal samples. The computational complexity and time performance of the proposed method is analyzed. The PETS, UMN and UCSD benchmark datasets are employed to verify the advantages of the proposed method for both global abnormal and local abnormal event detection. This method can be used for event detection for a surveillance video and outperforms the state-of-the-art algorithms. Thus it can be adopted to detect the abnormal event in the monitoring video.
关键词Global abnormal event Local abnormal event Multi-RoI Covariance matrix Optical flow
DOI10.1007/s11042-017-5309-2
收录类别SCI
语种英语
资助项目Fundamental Research Funds for the Central Universities[YWF-14-RSC-102]
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000439750300062
出版者SPRINGER
引用统计
被引频次:33[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/32595
专题新能源学院
电气工程与信息工程学院
通讯作者Zhu, Aichun
作者单位1.Beihang Univ, Sch Automat Sci & Elect Engn, Beihang, Peoples R China
2.Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China
3.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou, Gansu, Peoples R China
4.Univ Technol Troyes, Inst Charles Delaunay, LM2S, UMR STMR 6279,CNRS, Troyes, France
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GB/T 7714
Wang, Tian,Qiao, Meina,Zhu, Aichun,et al. Abnormal event detection via covariance matrix for optical flow based feature[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2018,77(13):17375-17395.
APA Wang, Tian,Qiao, Meina,Zhu, Aichun,Niu, Yida,Li, Ce,&Snoussi, Hichem.(2018).Abnormal event detection via covariance matrix for optical flow based feature.MULTIMEDIA TOOLS AND APPLICATIONS,77(13),17375-17395.
MLA Wang, Tian,et al."Abnormal event detection via covariance matrix for optical flow based feature".MULTIMEDIA TOOLS AND APPLICATIONS 77.13(2018):17375-17395.
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