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 |
ISSN | 1380-7501 |
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 |
DOI | 10.1007/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 | |
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 |
推荐引用方式 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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