Optimization Strategy for Intelligent Traffic Monitoring Systems Based on Image Recognition

With the rapid urbanization, traffic congestion and safety issues have become increasingly prominent, and traditional traffic monitoring systems are struggling to meet the demands of modern traffic management. Intelligent traffic monitoring systems based on image recognition can significantly enhance the efficiency and safety of traffic management by analyzing traffic surveillance video data in real-time. While research in vehicle behavior recognition using intelligent traffic monitoring systems has made some progress, issues such as insufficient recognition accuracy, Areal-time performance, and challenges in multi-target detection and tracking remain. To address these problems, this paper proposes an intelligent traffic monitoring image-based vehicle behavior recognition algorithm. First, the overall framework of the algorithm is presented. Next, it provides a detailed introduction on constructing spatiotemporal feature bodies and analyzing the spatiotemporal feature representations of traffic surveillance videos. It then discusses the classification algorithm based on multi- core support vector machines (SVM). Finally, the algorithm's effectiveness and superiority are verified and analyzed through experimental results. This study not only enriches the theoretical framework of intelligent traffic monitoring technologies but also holds significant practical value for widespread application.