期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：4
页码：115-126
DOI：10.14257/ijsip.2014.7.4.11
出版社：SERSC
摘要：Abnormal event detection in nature settings is an active issue in computer vision domain. A novel unsupervised method is proposed to detect abnormal events by combining dynamic texture and sparse coding. In this method, dynamic texture is used as descriptors in a spatio- temporal manner to describe spatio-temporal volumes of events in videos. Sparse coding is utilized for reconstructing the testing data to measure its normalness. Experiments are conducted on the well known UCSD dataset and UMN dataset to demonstrate the efficiency of the proposed method. The results show that the proposed method outperforms the current state-of-the-art methods.
