Abstract: Modern motion description techniques for crowd motion in videos are mostly velocity descriptors based on optical flow.However,acceleration contains a wealth of motion information,which can provide information that the velocity descriptors are missing when describing complex motion patterns,and can better characterize complex motion patterns.This paper studies a motion descriptor,which uses an energy-based restricted Boltzmann machine model to perform anomalous behavior detection.Firstly,the optical flow information in the video is extracted,and the acceleration information is calculated through the optical flow information of two consecutive frames.Then,acceleration histogram feature is computed over spatial-temporal blocks,and all the spatial-temporal block histogram features of adjacent frames are spliced to obtain an acceleration descriptor.The Restricted Boltzmann Machine learns the normal motion patterns from the normal video training set,which is used for abnormal detection in terms of the errors of reconstructed data in detecting phase.The results show that the average area under the curve (AUC) of the UMN dataset reaches 0.984,and the area under the average curve (AUC) of UCF-Web reaches 0.958.Compared with other state-of-the-art algorithms,the proposed descriptor has superior performance on anomaly detection.

Key words: Abnormal behavior, Acceleration optical flow, Feature extraction, Motion feature, Restricted boltzmann machine