ANOMALY DETECTION IN SURVEILLANCE VIDEO USING MOTION DIRECTION STATISTICS

被引：24

作者：

Liu, Chang ^{[1
]}

Wang, Guijin ^{[1
]}

Ning, Wenxin ^{[1
]}

Lin, Xinggang ^{[1
]}

Li, Liang ^{[2
]}

Liu, Zhou ^{[2
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[2] Sony China Res Lab, Beijing 100190, Peoples R China

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING | 2010年

关键词：

Visual surveillance; Anomaly detection; Motion vector; One-class SVM; PCA;

D O I：

10.1109/ICIP.2010.5651958

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts:(a) a dense motion field and motion statistics method, (b) one-class SVM for one-class classification, (c) motion directional PCA for feature dimensionality reduction. Experiments demonstrate the effectiveness of proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Moreover, it works well in complicated situation where the common tracking or detection module won't work.

引用

页码：717 / 720

页数：4