Vehicle re-identification method based on multi-attribute dense linking network combined with distance control module

Abstract

IntroductionVehicle re-identification is a crucial task in intelligent transportation systems, presenting enduring challenges. The primary challenge involves the inefficiency of vehicle re-identification, necessitating substantial time for recognition within extensive datasets. A secondary challenge arises from notable image variations of the same vehicle due to differing shooting angles, lighting conditions, and diverse camera equipment, leading to reduced accuracy. This paper aims to enhance vehicle re-identification performance by proficiently extracting color and category information using a multi-attribute dense connection network, complemented by a distance control module.MethodsWe propose an integrated vehicle re-identification approach that combines a multi-attribute dense connection network with a distance control module. By merging a multi-attribute dense connection network that encompasses vehicle HSV color attributes and type attributes, we improve classification rates. The integration of the distance control module widens inter-class distances, diminishes intra-class distances, and boosts vehicle re-identification accuracy.ResultsTo validate the feasibility of our approach, we conducted experiments using multiple vehicle re-identification datasets. We measured various quantitative metrics, including accuracy, mean average precision, and rank-n. Experimental results indicate a significant enhancement in the performance of our method in vehicle re-identification tasks.DiscussionThe findings of this study provide valuable insights into the application of multi-attribute neural networks and deep learning in the field of vehicle re-identification. By effectively extracting color information from the HSV color space and vehicle category information using a multi-attribute dense connection network, coupled with the utilization of a distance control module to process vehicle features, our approach demonstrates improved performance in vehicle re-identification tasks, contributing to the advancement of smart city systems.