Abnormal event detection based on sparse reconstruction in crowded scenes

Abstract

In this paper, we propose an algorithm of abnormal event detection in crowded scenes using sparse representation over the bases of normal motion feature descriptors. To construct an over-complete dictionary, we extract the histogram of maximal optical flow projection (HMOFP) feature from a set of normal training frames. Then the K-SVD dictionary training method is used to get a redundant dictionary 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 U-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.