Improving Multi-view Vehicle Identification in Complex Scenes using Robust Deep Neural Networks

Vehicle re-identification is crucial for intelligent transportation systems and traffic management, enabling the matching of vehicles across diverse camera captures. The primary challenge lies in the significant variation in appearance and background due to different camera angles, which complicates the retrieval of consistent vehicle images from a database. The variability directly impacts the effectiveness of re-identification techniques. This paper proposes a novel learning approach that leverages a view-consistent triplet loss framework and region segmentation to address the challenges of pose variation and background complexity in vehicle re-identification due to multi-view imaging. Specifically, the consistency of segmentation area distribution is used to estimate view consistency, and then triplets are selected based on it. Concentrating on distinguishing features between sample pairs that are likely to be confused, our approach markedly enhances model robustness in scenarios involving multiple perspectives. Experimental evaluations on the Veri776 dataset demonstrate that the proposed method surpasses several state-of-the-art techniques across various metrics and shows exceptional performance in recognizing samples with complex viewpoints, thus validating the efficacy of our approach.