A method for realizing real-time abnormal behavior recognition based on compressed video
Cheng Yunfei, Wu Wang, Yuexia Liu · Advanced Optical Manufacturing Technologies and Applications 2022; and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022) · 2023
A robust algorithm is proposed for real-time abnormal behavior recognition in dynamic challenge including illumination change and pose variations. To cope with these factors, we present a new method through segmented sampling processing evenly divides the original video into several fragments and according to a certain sampling density, the description of one I frame and the description information of several P frames are obtained from each video clip. Through the Res2Net18 network, a behavior classifier based on the cumulative motion vector of P frames and a behavior classifier based on the cumulative residuals of P frames are built. The various frame information extracted from each video clip is entered into the corresponding network, and each network outputs a classification score. According to the type of frame information entered, the classification score based on each type of input in all video clips is summed and averaged, and the classification score based on each type of input is obtained at the original video level. The ensemble is carried out by means of weighted summation, and the total classification score is obtained as the output of the abnormal behavior recognition network. Experimental results on benchmark datasets demonstrate that the proposed method performs robustly and favorably.