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摘要
In this paper, a new method using level set is proposed for abnormal event detection and localization in video surveillance. From an input video, five image descriptors, namely the color moments, the edge histogram descriptors, the color and edge directivity descriptors, the color layout descriptors, and the scalable color descriptors, are extracted for robust detection. We employ the local binary fitting model as the statistical learning model to update by the input videos in real time. We performed experiments on the publicly available UCSD anomaly detection dataset and showed that our method has good performance for detecting and localizing abnormality compared to the state-of-the-art methods.
- 出版日期2018-2
- 单位中国石油大学（北京）; 中国石油化工股份有限公司青岛安全工程研究院
