复杂交通监控场景下的轻量级行人目标追踪技术

his article aims to propose a lightweight pedestrian target tracking technology for complex traffic monitoring scenarios, in order to improve its real-time performance and accuracy. In complex traffic scenarios, the rapid movement and frequent occlusion of pedestrian targets pose higher requirements for tracking algorithms. Therefore, the study adopts a lightweight feature extraction method, which optimizes the feature extraction network structure to reduce computational resource consumption while ensuring the robustness and discriminability of features. Combined with the uniform acceleration Kalman filter module, this module can effectively predict the motion trajectory of pedestrian targets, model the uniform acceleration motion of targets, and provide reliable predicted positions when targets are lost or obstructed. The experimental results show that the proposed lightweight tracking technology exhibits good real-time performance and accuracy in complex traffic monitoring scenarios. Compared with the original DeepSORT algorithm, MOTA improves by about 2.1%, HOTA improves by about 1.9%, and the average processing speed increases by about 15 frames per second. This technology has significant advantages in resource constrained monitoring systems, effectively reducing computational costs while maintaining high tracking performance. In summary, this lightweight pedestrian target tracking technology provides an efficient and reliable solution for complex traffic monitoring scenarios, with broad application prospects.?????