Abnormal event detection in crowded scenes using two sparse dictionaries with saliency

Abstract

Abnormal event detection in crowded scenes is a challenging problem due to the high density of the crowds and the occlusions between individuals. We propose a method using two sparse dictionaries with saliency to detect abnormal events in crowded scenes. By combining a multiscale histogram of optical flow (MHOF) and a multiscale histogram of oriented gradient (MHOG) into a multiscale histogram of optical flow and gradient, we are able to represent the feature of a spatial–temporal cuboid without separating the individuals in the crowd. While MHOF captures the temporal information, MHOG encodes both spatial and temporal information. The combination of these two features is able to represent the cuboid’s appearance and motion characteristics even when the density of the crowds becomes high. An abnormal dictionary is added to the traditional sparse model with only a normal dictionary included. In addition, the saliency of the testing sample is combined with two sparse reconstruction costs on the normal and abnormal dictionary to measure the normalness of the testing sample. The experiment results show the effectiveness of our method.