Detecting the Degree of Anomal in Security Video

We have developed a method that can discriminate anomalous image sequences for more efficiently utilizing security videos. To match the wide popularity of security cameras, the method is independent of the camera setting environment and video contents. We use the spatio-temporal feature obtained by extracting the areas of change from the video. To create the input for the discrimination process, we reduce the dimensionality of the data by PCA. Discrimination is based on a 1-class SVM, which is a non-supervised learning method, and its output is the degree of anomaly of the sequence. In experiments we apply the method to videos obtained by a network camera; the results show the feasibility of indexing anomalous sequences from security videos.

• Type: Conference
• Year: 2007
• Where: MVA
• Authors: Kyoko Sudo, Tatsuya Osawa, Xiaojun Wu, Kaoru Wakabayashi, Takayuki Yasuno
• Keywords: Anomalous Image Sequences, Camera Setting Environment, Computer Vision, MVA 2007, Security Videos