Sparse coding based motion attention for abnormal event detection

In this paper, we present a novel method based on sparsely coded motion attention for detecting abnormal events in crowded scenes. Unlike existing sparse coding based approaches, our model does not need to learn a dictionary and directly sparsely codes the motion features of the center patches with features of its surrounding patches. The sparse coding error is used to measure the motion attention intensity of the center patch. To reflect the crowd abnormal intensity, an online updated weighting scheme is designed to obtain the global activity intensity map. Two publicly available datasets-UMN dataset and UCSD Ped1 dataset are utilized to evaluate our approach in detecting global abnormal event and local abnormal event, respectively. The experiments show our method achieves the promising performance and is competitive with the state-of-the-art approaches.