Comparison and analysis of deep learning models for vehicle re-identification

Abstract

The rapid urbanization and increasing number of vehicles on the roads demand efficient and accurate vehicle re-identification (Re-ID) techniques for intelligent transportation systems, traffic monitoring, and surveillance applications. This paper offers a detailed analysis of deep learning approaches to vehicle Re-ID, covering feature learning, attention mechanism, unsupervised learning, self-supervised learning, and specialized loss function. The efficiency of these methods is assessed using VeRi-776 and VehicleID datasets. Key metrics, such as mean Average Precision (mAP) and Rank-n accuracy, are employed to gauge their success. Results show that feature learning, attention mechanism, and specialized loss function play pivotal roles in achieving high performance in vehicle Re-ID tasks, with unsupervised and self-supervised learning methods displaying potential for practical applications due to their scalability. The paper also highlights several challenges, including enhancing the interpretability of attention mechanisms, exploring the relationship between popular loss functions, and addressing the infeasibility of existing methods for real-time applications. The paper concludes with several recommendations for the prospects of vehicle Re-ID, including developing advanced real-time algorithms, enhancing deep learning techniques, investigating innovative approaches, and addressing existing challenges. These proposed advances could significantly improve the efficacy of feature learning and spark fresh innovations in this field.