Development of an Intelligent Tracking System for Pedestrian Re-identification Based on Deep Learning

In the context of the continuous development of society and the accelerating process of urbanization, public security issues have become increasingly prominent. Pedestrian re-identification technology and pedestrian tracking technology, with their unique advantages, play a crucial role in the field of public security. Pedestrian re-identification aims to accurately determine the identity of pedestrians by breaking through various limitations, while pedestrian tracking focuses on precisely tracking the trajectories of pedestrians in consecutive video frames. In view of this, this research is dedicated to developing and implementing a pedestrian tracking system based on pedestrian re-identification technology. Through the integration of cutting-edge technologies such as YOLOv8 and DeepSORT, and after repeated experiments and optimizations, an intelligent tracking system covering multiple key modules such as data preprocessing, pedestrian detection, and pedestrian re-identification has been successfully constructed. Experimental data shows that in practical scenarios such as complex illumination, occlusion, and rapid movement of targets, the accuracy and tracking success rate of this system remain above 90%, demonstrating good robustness and ensuring stable tracking effects. At the same time, its innovative algorithms and model architectures provide reference examples and new ideas for research in related fields such as computer vision and intelligent security, promoting the overall development of industry technology.