跨域联合学习与共享子空间度量的车辆重识别  

Cross-domain joint learning and shared subspace metric for vehicle re-identification

作　　者：汪琦 雪心远 闵卫东 汪晟 盖迪 韩清 Wang Qi;Xue Xinyuan;Min Weidong;Wang Sheng;Gai Di;Han Qing(School of Mathematics and Computer Science,Nanchang University,Nanchang 330031,China;School of Software,Nanchang University,Nanchang 330047,China;Institute of Metaverse,Nanchang University,Nanchang 330031,China;Jiangxi Key Laboratory of Smart City,Nanchang 330031,China)

机构地区：[1]南昌大学数学与计算机学院,南昌330031 [2]南昌大学软件学院,南昌330047 [3]南昌大学元宇宙研究院,南昌330031 [4]江西省智慧城市重点实验室,南昌330031

出　　处：《中国图象图形学报》2024年第5期1364-1380,共17页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62076117,62166026);江西省智慧城市重点实验室项目(20192BCD40002);江西省自然科学基金项目(20224BAB212011,20232BAB212008,20232BAB202051)。

摘　　要：目的 现有的跨域重识别任务普遍存在源域与目标域之间的域偏差大和聚类质量差的问题,同时跨域模型过度关注在目标域上的泛化能力将导致对源域知识的永久性遗忘。为了克服以上挑战,提出了一个基于跨域联合学习与共享子空间度量的车辆重识别方法。方法 在跨域联合学习中设计了一种交叉置信软聚类来建立源域与目标域之间的域间相关性,并利用软聚类结果产生的监督信息来保留旧知识与泛化新知识。提出了一种显著性感知注意力机制来获取车辆的显著性特征,将原始特征与显著性特征映射到一个共享子空间中并通过它们各自全局与局部之间的杰卡德距离来获取共享度量因子,根据共享度量因子来平滑全局与局部的伪标签,进而促使模型能够学习到更具鉴别力的特征。结果 在3个公共车辆重识别数据集VeRi-776(vehicle re-identification-776 dataset)、VehicleID(largescale vehicle re-identification dataset)和VeRi-Wild(vehicle re-identification dataset in the wild)上与较新方法进行实验对比,以首位命中率(rank-1 accuracy,Rank-1)和平均精度均值(mean average precision,mAP)作为性能评价指标,本文方法在VeRi-776→VeRi-Wild,VeRi-Wild→VeRi-776,VeRi-776→VehicleID,VehicleID→VeRi-776的跨域任务中,分别在目标域中取得了42.40%,41.70%,56.40%,61.90%的Rank-1准确率以及22.50%,23.10%,41.50%,49.10%的mAP准确率。在积累源域的旧知识表现中分别取得了84.60%,84.00%,77.10%,67.00%的Rank-1准确率以及55.80%,44.80%,46.50%,30.70%的mAP准确率。结论 相较于无监督域自适应和无监督混合域方法,本文方法能够在积累跨域知识的同时有效缓解域偏差大的问题,进而提升车辆重识别的性能。Objective Vehicle re-identification(Re-ID)is a technology that uses computer vision technology to determine whether a specific target vehicle exists in an image or video sequence, which is considered a subproblem of image retrieval.Vehicle Re-ID technology can be used to monitor specific abandoned vehicles and prevent driving escape and is widelyapplied in the fields of intelligent surveillance and transportation. The previous methods mainly focused on supervised train⁃ing in a single domain. If the effective Re-ID model in the single domain is transferred to an unlabeled new domain for test⁃ing, retrieval accuracy will significantly decrease. Some researchers have gradually proposed many cross-domain-basedRe-ID methods to alleviate the manual annotation cost of massive surveillance data. This study aims to transfer the trainedsupervised Re-ID model from the labeled source domain to the unlabeled target domain for clustering. The entire transferprocess uses unsupervised iteration and update of model parameters, thereby achieving the goal of reducing manual annota⁃tion costs. However, the existing cross-domain Re-ID tasks generally have two main challenges: on the one hand, the exist⁃ing cross-domain Re-ID methods focus too much on the performance of the target domain, often neglecting the old knowl⁃edge previously learned in the source domain, which will cause catastrophic forgetting of the old knowledge. On the otherhand, the large deviation between the source and target domains will directly affect the generalization ability of the Re-IDmodel mainly because of the significant differences in data distribution and domain attributes in different domains. Hence,a vehicle Re-ID method based on cross-domain joint learning and a shared subspace metric is proposed to overcome theabove challenges. Method First, a cross-confidence soft cluster is designed in cross-domain joint learning to establish theinter-domain correlation between the source and target domains. The cross-confidence soft cluster aims to intro

关 键 词：车辆重识别 跨域联合学习(CJL) 交叉置信软聚类 共享子空间度量(SSM) 显著性感知注意力机制 伪标签平滑 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]