Research on Video Synopsis Based on Deep Learning Target Detection and Target Trajectory Optimization

Abstract

 In this era of rapid economic and technological development, monitoring is essential for security issues. However, it is also accompanied by the low efficiency of managing and viewing a large number of surveillance videos every day. Traditional methods are gradually unable to meet the requirements of application, and the video field requires algorithm technology that can better solve practical problems. Intelligent monitoring technology is gradually emerging and developing, based on theories such as computer vision, which can solve many monitoring video problems and greatly improve the efficiency of monitoring video work. Through in-depth exploration of existing video synopsis technology algorithms, this article proposes a new video synopsis method that can effectively detect and track videos, thereby greatly improving the efficiency of storage, transmission, and use of surveillance videos. Experiments have shown that this method can effectively concentrate surveillance videos, and compared with existing methods, it has a better synopsis ratio while ensuring the integrity of video information, effectively reducing collisions between targets, effectively reducing overlap between targets, and achieving good visual effects. Utilizing an improved deep learning object detection and multi object tracking algorithm with added attention mechanism to extract foreground moving targets in videos, and using a mixed Gaussian background modeling algorithm establish a background, laying a stable target foundation for subsequent video concentration. At the same time, design trajectory recombination optimization methods to ensure that the targets do not overlap as much as possible. Reasonably place the targets in the new condensed video sequence, determine the index of all targets in the new condensed video stream, and finally integrate each frame of target images into the background image according to the set index rules, ultimately obtaining the synopsis video.