An Abnormal Behavior Detection Method using Optical Flow Model and OpenPose

Abstract

Abnormal behavior detection and recognition of pedestrian in escalator has always been a challenging task in intelligent video surveillance system. To cope this problem, a method combining optical flow vector of passenger with human skeleton extraction is proposed. At first, adaptive dual fractional order optical flow model is used to estimate the optical flow field under scenes with illumination changes, low contrast and uneven illumination. At the same time, the OpenPose deep convolutional neural network is used to extract body skeleton and persons in image can be located. Then, the optical flow field and the human skeleton are combined to obtain the optical flow vector of the passenger head. After that the optical flow field of the passenger head and the step of escalator under the passenger foot are used for abnormal behavior detection and recognition, random forest is employed to behavior classifier. Experimental results show that our proposed method and its improvement strategy can accurately estimate the optical flow field in real time of low contrast outdoor videos with insufficient illumination, uneven brightness and illumination changes, the accuracy of abnormal action detection and recognition can reach to 97.98% and 92.28%.