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-...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.展开更多
文摘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.
