Abnormal detection in crowded scenes via kernel based direct density ratio estimation

This paper presents a new inlier-based outlier detection scheme for the analysis of abnormal behavior in crowded scenes. First, we segment the video into a set of cubes, and then extract the three feature descriptors from each cube, including the histogram of oriented gradients (HOG), the histogram of motion directions, and the motion magnitude descriptors. Thereafter, for each feature descriptor the Kullback-Leibler importance estimation procedure (KLIEP) is employed to compute the ratio of test and training densities, referred to as the importance, instead of computing these two densities separately. This allows us to avoid the difficulty in estimating complex probability density estimation. The importance denotes an inlier score which represents the degree of similarity between the test and training data. Based on the importance of each descriptor and a prespecified threshold in each cube, we can then identify if a cube contains an anomaly event. Through computer simulations, we find that the new approach provides high accuracy of localization rate based on the widespread UCSD datasets compared with previous works.