Vehicle Re-Identification Strategy Based on Cross Domain Dual Branch Adversarial Networks
In order to reduce the domain bias and improve the application generalization ability of the algorithm,a vehicle re-identification strategy based on cross domain dual branch adversarial networks was proposed.Firstly,the marked data of the source domain were fully mined to adapt to the target domain to reduce the cross domain bias,and an image to image conversion network called dual branch confrontation network was proposed to effectively retain the attributes of images from the source do-main.In addition,a feature learning model based on structural attention mechanism was proposed to extract salient features while suppressing background.Finally,the experimental results of two vehicle re-identification data sets show that the proposed method can achieve high precision vehicle re-identification effect,and has good generalization ability.
NETL
NSTL
万方数据
维普
