Vehicle Re-identification System using Residual Network with Instance-Batch Normalization

Vehicle Re-identification (Re-ID) has become extremely important due to the increasing number of vehicles on the road and its potential to address traffic-related challenges. As a result, there is also a growing need for efficient methods to track and identify vehicles across multiple traffic cameras. One of the biggest challenges of this task is the variations in vehicle appearances across different camera angles. This is because vehicles can appear significantly different when captured from various camera angles and viewpoints. Furthermore, the current vehicle Re-ID solutions typically require extensive coding knowledge, making it inaccessible to many potential users. Therefore, we focus on developing a user-friendly software application that simplifies the entire Re-ID workflow. This includes tasks like dataset preparation and data preprocessing using YOLO, model training with ResNet-ibn, performance evaluation, and visualization of results. The application provides a comprehensive pipeline that enables users to perform vehicle Re-ID tasks without requiring advanced programming skills. The experiment results shown that ResNet-IBN model achieved the highest results on custom dataset MMUVD_1500 with mAP of 87.63% and Rank@1 of 84.68% respectively. For instance, users would be able to input query vehicle images and receive matched gallery images from different camera viewpoints through the application interface. Thus, this makes it easier for users to track vehicles across multiple locations, enhance the usability and broaden the accessibility of vehicle Re-ID tasks. The final outcome is a complete software solution with a user-friendly interface that allows users to perform vehicle Re-ID tasks effortlessly.