VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification

Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angles leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world. To promote the Vehicle Re-ID models, this paper proposes to synthesize a large number of vehicle images in the target pose, whose idea is to project the vehicles of diverse poses into the unified target pose so as to enhance feature discrimination. Considering that the paired data of the same vehicles in different traffic surveillance cameras might be not available in the real world, we propose the first Pair-flexible Pose Guided Image Synthesis method for Vehicle Re-ID, named as VehicleGAN in this paper, which works for both supervised and unsupervised settings without the knowledge of geometric 3D models. Because of the feature distribution difference between real and synthetic data, simply training a traditional metric learning based Re-ID model with data-level fusion (i.e., data augmentation) is not satisfactory, therefore we propose a new Joint Metric Learning (JML) via effective feature-level fusion from both real and synthetic data. Intensive experimental results on the public VeRi-776 and VehicleID datasets prove the accuracy and effectiveness of our proposed VehicleGAN and JML.

I. Introduction

Many tasks and functions in the intelligent transportation systems [1]–[13] involve the detection or identification of vehicles. Vehicle Re-identification (Re-ID) is an important task in intelligent transportation systems, as it allows for the retrieval of the same vehicle from multiple non-overlapping cameras. With the availability of vehicle surveillance datasets [14]–[16], many vehicle Re-ID methods [17]–[19] have been proposed, which have gained wide interest among the research communities of foundation intelligence, human-machine systems, and transportation [8], [20], [21].