面向跨域行人再识别的虚拟数据生成与选择  

Generation and selection of synthetic data for cross-domain person re-identification

作　　者：蔡益武 张雨佳 张永飞[1,2] CAI Yi-wu;ZHANG Yu-jia;ZHANG Yong-fei(Beijing Key Laboratory of Digital Media,School of Computer Science and Engineering,Beihang University,Beijing 100191,China;State Key Laboratory of Virtual Reality Technology and Systems,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学计算机学院北京市数字媒体实验室,北京100191 [2]北京航空航天大学虚拟现实技术与系统国家重点实验室,北京100191

出　　处：《图学学报》2023年第4期775-783,共9页Journal of Graphics

基　　金：国家自然科学基金项目(62072022)。

摘　　要：针对当前基于深度学习的行人再识别模型依赖于大量标注数据的训练,其收集和标注代价极高;而现有的行人图像数据生成方法未考虑目标域数据特点,跨域性能有待提升的问题,提出一种面向跨域行人再识别的虚拟数据生成与选择算法。首先利用目标域前景信息如行人着装颜色分布指导虚拟3D人体模型生成,获得与真实人物整体着装较为相似的虚拟人物。接着引导模型专注于通过前景信息区分不同行人,在生成的虚拟数据上替换目标域背景信息,达到在像素级上提高源域数据质量的目的。最后,根据分布度量如Wasserstein Distance等度量源域和目标域的特征分布距离,在特征级上选择与目标域最接近的源域训练子集用以模型训练。实验结果表明,该方法优于现有的其他行人数据生成算法,可以显著提升行人再识别模型的跨域泛化性能。The reliance of mainstream deep learning-based person re-identification models on large-scale labeled data for training is a costly process that requires extensive collection and labeling efforts.Additionally,the existing virtual data generation methods neglect to account for the characteristics of target domain,thereby compromising the performance of cross-domain re-identification.To address these issues,this paper proposed a synthetic data generation and selection algorithm for cross-domain person re-identification.First,this algorithm utilized the foreground information of the target domain,including the color distribution of individuals’clothing,to guide the generation of virtual 3D human models.The background information of the target domain was employed to replace the background of source domain data.This served to enhance the data quality at the pixel level,while also guiding the model to distinguish different persons based on the foreground.Finally,the proposed method employed distribution metrics such as Wasserstein Distance to measure the feature distribution distance between the source domain and target domain.This distance was used to select the source domain training subset closest to the target domain for model training.The experimental results demonstrated the superiority of this method over other existing person virtual data generation algorithms,as it can significantly improve the cross-domain generalization performance of the person re-identification model.

关 键 词：行人再识别 数据生成 虚拟引擎 数据选择 分布度量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]