Exploring Deep Learning Techniques for Vision-Based Vehicle Re-Identification: A Traffic Intersection Case Study

We present a comprehensive review of recent deep learning techniques for vehicle re-identification (Re-ID) in Intelligent Transportation Systems (ITS). Vehicle Re-ID involves recognizing and matching specific vehicles across different cameras, against challenges such as pose variation, illumination, and weather. The study summarizes vision-based approaches, discusses challenges involved in real-time applications, and provides a brief comparison of methodologies and datasets available for the task. Additionally, a case study showcases an application of vehicle Re-ID at traffic junctions for the purpose of enforcing traffic rules, including identifying traffic light violations and capturing clear views of license plates for violator identification. By providing a summary of existing work and suggesting future research directions, we aim to assist researchers in advancing intelligent transportation systems and contributing to the development of smarter cities.