City Scale Multi Camera Vehicle Re-Identification

Abstract

Vehicle re-identification (Re-ID) is a difficult computer vision task that requires matching and tracking cars across many cameras with no overlapping views. Its applications in traffic monitoring, law enforcement, and intelligent transportation systems have piqued the interest of many people. This work addresses the issues of vehicle re-identification by merging vehicle, orientation, and camera re-identification approaches. This research aims to overcome the limits of existing vehicle re-identification systems, which struggle with comparable backgrounds, shapes, and orientations. These characteristics contribute significant bias in similarity evaluation and impede the acquisition of fine-grained information. To address these challenges and improve the accuracy of vehicle re-identification, the study offers VOC-ReID, an orientation re-identification approach. Furthermore, VGG-NeT with Global Average Pooling is used to extract more distinguishing characteristics from vehicle photos. The triplet of vehicle-orientation-camera is considered a unitary entity by VOC-ReID, changing the focus from backdrop and form to re-identification. Numerous experiments were carried out utilizing large-scale vehicle re-identification datasets, including real-world surveillance film, to assess the effectiveness of the VOC-ReID technique. In comparison to our baseline technique, we observed a slight improvement in our results. In our approach, we divided the entire image into 360 bins and determined the direction of each bin. These directions were utilized as orientation labels in the cityFlow dataset. When we applied our technique to the cityFlow dataset, we achieved an accuracy of 67% in terms of mean Average Precision (mAP). The accuracy of the method was assessed using mean Average Precision (mAP) and Rank-1 accuracy measures.