Vehicle Re-identification (Re-ID) has drawn extensive exploration recently; nevertheless, the issue of accurately distinguishing features in latent space across varying vehicle poses, remains a challenging hurdle for real-world application of Vehicle Re-ID. To address this challenge, we supply a novel idea which projects the various-pose vehicle images into a unified target pose so as to promote the discriminative capability of vehicle Re-ID model. Acknowledging the labor and cost of paired data for the same vehicle images across different traffic surveillance cameras in practical scenarios, we propose the pioneering Pair-flexible Pose Guided Image Synthesis for vehicle Re-ID, denominated as VehicleGAN. Our method is adept at both supervised (paired images of same vehicle) and unsupervised (unpaired images of any vehicle) settings, and bypasses the need of geometric 3D model information. Furthermore, we propose a novel Joint Metric Learning (JML) method to facilitate the effective fusion of both real and synthetic data. Comprehensive experimental analyses conducted on the public VeRi-776 and VehicleID datasets substantiate the precision and efficacy of our proposed VehicleGAN and JML.

Deep learning-based intelligent vehicle perception has been developing prominently in recent years to provide a reliable source for motion planning and decision making in autonomous driving. A large number of powerful deep learning-based methods can achieve excellent performance in solving various perception problems of autonomous driving. However, these deep learning methods still have several limitations, for example, the assumption that lab-training (source domain) and real-testing (target domain) data follow the same feature distribution may not be practical in the real world. There is often a dramatic domain gap between them in many real-world cases. As a solution to this challenge, deep transfer learning can handle situations excellently by transferring the knowledge from one domain to another. Deep transfer learning aims to improve task performance in a new domain by leveraging the knowledge of similar tasks learned in another domain before. Nevertheless, there are currently no survey papers on the topic of deep transfer learning for intelligent vehicle perception. To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception. This paper discusses the domain gaps related to the differences of sensor, data, and model for the intelligent vehicle perception. The recent applications, challenges, future researches in intelligent vehicle perception are also explored.