Global anomaly detection in crowded scenes based on optical flow saliency

Abstract

In this paper, an algorithm of global anomaly detection in crowded scenes using the saliency in optical flow field is proposed. Before the process of extracting the histogram of maximal optical flow projection (HMOFP), the scale invariant feature transforms (SIFT) method is utilized to get the saliency map of optical flow field. On the basis of the HMOFP feature of normal frames, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy after a process of selecting the training samples, which is better than the dictionary simply composed by the HMOFP feature of the whole training frames. In order to detect whether a frame is normal or not, we use the ℓ1-norm of the sparse reconstruction coefficients (i.e., the sparse reconstruction cost, SRC) to show the anomaly of the testing frame, which is simple but very effective. The experiment results on UMN dataset and the comparison to the state-of-the-art methods show that our algorithm is promising.