Surveillance video synopsis framework base on tube set

Abstract

Video synopsis technology can shorten the length of the video, which has attracted wide attention. However, due to the limitation of object extraction technology and the difficulty of preserving interactivity, synopsis videos will lose the semantic information of the original video. To address the above problems, we propose a video synopsis framework based on tube sets. Firstly, we propose a video tracking algorithm based on Yolov4 and Kalman Filter, which can effectively alleviate the problem that it is difficult for object tracking technology to extract occluded objects. Secondly, we propose a method that combines moving direction and dynamic threshold to judge interactivity. Accurately judging interactivity can ensure that the interactive objects are not separated in the synopsis video. Finally, we propose a tube set mapping model (TSMM) that can rearrange with the tube set as the basic unit. Experimental results demonstrate that our method can achieve superior performance than other state-of-the-art methods.