Enhancing Efficiency and Accuracy in Video Surveillance Systems: A Target Detection Model Utilizing Open Pose Algorithm

Abstract

Video surveillance plays a critical role in maintaining public security by offering real-time, comprehensive, and easily interpretable image information. While traditional surveillance relies heavily on human operators who are susceptible to visual fatigue, intelligent video surveillance systems have emerged as a solution to enhance monitoring efficiency and effectiveness. This paper explores the development and application of a video surveillance system model leveraging the Open Pose algorithm for target detection. The model's ability to detect and track multiple targets, recognize abnormal behaviors, and assess crowd and traffic flow is highlighted. Experimental results demonstrate significant improvements in monitoring efficiency and accuracy, although challenges remain in preprocessing under unstable environmental conditions. Future work will focus on enhancing the preprocessing module to address these challenges and further improve system performance.