Abstract: In order to accurately locate the abnormal behavior, an anomaly detection method based on time context features of super-pixels is proposed. For feature representation, the video frames are firstly segmented into super-pixels. The super-pixels of foreground are then selected according to their pixel ratios of foreground. Super-pixels matching adjacent frames are selected based on the gray-level histogram and the information of location to enhance the temporal context of super-pixel features. The statical value of multilayer histogram of optical flow of matched super-pixels are taken as the feature for detection. In the phase of detection, the sparse combination learning algorithm is adopted to detect abnormality. Experimental results show that the algorithm outperforms other state-of-the-art algorithms in the UCSD and UMN video libraries.

Key words: anomaly detection, super-pixels, time context feature, sparse combination learning