Anomaly detection in crowded scenes by SL-HOF descriptor and foreground classification
With the widespread use of surveillance cameras, massive video data analysis has become an extremely labor-intensive work. In this paper, we propose an efficient approach to detect video anomaly in crowded scenes based on Spatially Localized Histogram of Optical Flow (SL-HOF) descriptor and foreground classification. For motion description, the new SL-HOF descriptor can not only preserve classic HOF descriptor's favorable capability of characterizing the motion velocity and direction of foreground in crowded scene, but also depicts the spatial distribution of optical flow, which implicitly encodes the structure and local motion information of foreground objects in videos. SL-HOF is shown to significantly outperform other classic video descriptors. To further boost the performance of anomaly localization, we then introduce Robust PCA based foreground classification to discriminate anomalous foreground texture. Instead of computationally expensive approaches like l1-norm Sparse Coding, we adopt classic one-class SVM (OCSVM) to model normal video events and detect outliers (anomaly). Our experiments on the challenging UCSD datasets show our approach can achieve state-of-the-art results when compared to existing video anomaly detection methods.