一种基于多摄像头的行人追踪和重识别方案

A Multi-Camera-Based Pedestrian Tracking and Re-Identification Solution

摘要: 随着城市交通管理、安保和智慧城市建设的需求日益增长，研究高效、准确、实时的行人定位和追踪系统具有重要的研究意义和实用价值。本文针对目前计算机视觉和人工智能领域的行人检测和跟踪技术，提出了一个基于视觉的行人定位和追踪系统。系统采用YOLOv5算法进行行人检测，DeepSORT算法进行多目标跟踪，并使用ResNet50模型实现行人重识别。此外，系统还支持目标运动轨迹绘制、行人数量统计等功能。实验结果表明，该系统在公开数据集(例如Market-1501数据集)和现实场景数据上具有良好的性能表现，可为城市交通管理、安保和智慧城市建设等领域提供有力支持。未来，将继续优化算法和模型结构，提高系统的准确性和实时性，为计算机视觉和人工智能领域的研究和应用带来更多创新。

Abstract: With the growing demand for urban traffic management, security, and smart city development, researching efficient, accurate, real-time pedestrian localization and tracking systems holds significant research significance and practical value. This paper proposes a vision-based pedestrian localization and tracking system targeting current pedestrian detection and tracking technologies in the fields of computer vision and artificial intelligence. The system employs the YOLOv5 algorithm for pedestrian detection, the DeepSORT algorithm for multi-object tracking, and the ResNet50 model for pedestrian re-identification. Additionally, the system supports functions such as target motion trajectory mapping and pedestrian counting. Experimental results demonstrate that the system exhibits excellent performance on both public datasets (e.g., the Market-1501 dataset) and real-world scenario data, providing robust support for urban traffic management, security, and smart city development. In the future, further optimization of algorithms and model structures will be pursued to enhance the system’s accuracy and real-time performance, driving innovation in both research and applications within the fields of computer vision and artificial intelligence.