Unsupervised vehicle re-identification via meta-type generalization  

作　　者：HUANG Chengti ZHANG Xiaoxiang ZHAO Qianqian ZHU Jianqing 黄诚惕

机构地区：[1]College of Engineering,Huaqiao University,Quanzhou 362021,P.R.China [2]College of Information Science and Engineering,Huaqiao University,Xiamen 361021,P.R.China

出　　处：《High Technology Letters》2025年第1期32-40,共9页高技术通讯(英文版)

基　　金：Supported by the National Natural Science Foundation of China(No.61976098);the Natural Science Foundation for Outstanding Young Scholars of Fujian Province(No.2022J06023).

摘　　要：Unsupervised vehicle re-identification(Re-ID)methods have garnered widespread attention due to their potential in real-world traffic monitoring.However,existing unsupervised domain adaptation techniques often rely on pseudo-labels generated from the source domain,which struggle to effectively address the diversity and dynamic nature of real-world scenarios.Given the limited variety of common vehicle types,enhancing the model’s generalization capability across these types is crucial.To this end,an innovative approach called meta-type generalization(MTG)is proposed.By dividing the training data into meta-train and meta-test sets based on vehicle type information,a novel gradient interaction computation strategy is designed to enhance the model’s ability to learn typeinvariant features.Integrated into the ResNet50 backbone,the MTG model achieves improvements of 4.50%and 12.04%on the Veri-776 and VRAI datasets,respectively,compared with traditional unsupervised algorithms,and surpasses current state-of-the-art methods.This achievement holds promise for application in intelligent traffic systems,enabling more efficient urban traffic solutions.

关 键 词：deep learning unsupervised vehicle re-identification(Re-ID) META-LEARNING 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]