Vehicle Re-Identification Using Deep Learning Methods with a Focus on Challenges and Recent Solutions
Keywords:
Machine Learning, Deep Learning, Computer vision, intelligent transportation system, Vehicle Re-identificationAbstract
Vehicle re-identification (Re-ID) is essential in intelligent transportation systems and surveillance, facilitating vehicle tracking and monitoring across diverse scenarios. In recent years, vehicle Re-ID has seen significant progress, primarily driven by advancements in deep learning. Therefore, this article aims to help researchers grasp the latest developments and identify future trends in the field. First, we explore the state-of-the-art deep learning techniques that have significantly enhanced the accuracy and efficiency of vehicle Re-ID, which have shown exceptional prowess in extracting and learning discriminative features from vehicle images. Additionally, we present a multidimensional classification that classifies existing deep learning-based vehicle re-identification methods into three groups: metric learning approaches, part-based approaches, and generative adversarial learning approaches. Finally, we address some challenges and suggest potential research directions for vehicle Re-ID. Through this paper, we aim to provide a nuanced understanding of the current landscape of deep-learning techniques in vehicle Re-ID, offering insights into their capabilities and limitations and paving the way for future research in this vital area.
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