Drastic Anomaly Detection in Video Using Motion Direction Statistics

作者：Liu Chang^*; Wang Guijin; Ning Wenxin; Lin Xinggang

来源：IEICE Transactions on Information and Systems, 2011, E94D(8): 1700-1707.

DOI：10.1587/transinf.E94.D.1700

• visual surveillance
• anomaly detection
• motion vector
• one-class SVM
• PCA

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) motion directional PCA for feature dimensionality reduction, (c) an improved one-class SVM for one-class classification. Experiments demonstrate the effectiveness of the proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Our scheme works well in complicated situations that common tracking or detection modules cannot handle.

• 出版日期2011-8
• 单位清华大学