精细化局部语义与属性学习的行人重识别  

Person Re-Identification Network with Fine-Grained Local Semantics and Attribute Learning

作　　者：肖进胜[1] 吴婧逸 郭浩文 郭圆 赵持恒 王银 XIAO Jin-Sheng;WU Jing-Yi;GUO Hao-Wen;GUO Yuan;ZHAO Chi-Heng;WANG Yin(School of Electronic Information,Wuhan University,Wuhan 430072;School of Resources and Environmental Engineering,Wuhan University of Technology,Wuhan 430070)

机构地区：[1]武汉大学电子信息学院,武汉430072 [2]武汉理工大学资源与环境工程学院,武汉430070

出　　处：《计算机学报》2024年第10期2387-2400,共14页Chinese Journal of Computers

基　　金：国家自然科学基金(42201480);国家重点研发计划(2021YFB2501104)资助。

摘　　要：行人的随身物品信息与属性描述是提高行人重识别任务性能的有效途径.本文提出了一种精细化局部语义与属性学习的行人重识别网络,来提取行人的随身物品信息,同时从语义区域中获得行人的属性描述.首先,将特征聚类方法生成的随身物品区域融合到额外语义模型生成的语义解析结果中,解决目前较多额外语义解析模型遗漏行人随身物品信息的问题.其次,利用生成的语义区域作为身体标签,网络由全局特征构建这些区域的语义特征映射,然后从语义特征中预测与之相关的属性信息,增强行人的描述.最后,考虑到行人某些属性之间包含强相关性,重新构建加权模型来提高某些属性的置信分数,优化属性的预测准确率.将属性预测结果和行人的全局特征连接在一起,形成行人的鲁棒特征表示.在Market-1501和DukeMTMC-reID属性数据集上的实验表明,所提算法较基线网络分别得到了3.6%和6.4%的mAP指标增益,可以提高行人重识别任务的性能.Pedestrian Re-IDentification(Person Re-ID) aims to search for the same pedestrian across multiple different camera views.Due to its importance in various practical applications such as video surveillance and content-based image retrieval,it has garnered extensive attention in recent years.However,the task still faces numerous challenges,including significant variations in pedestrian poses,lighting,and backgrounds in different camera views.Additionally,the similar appearance of clothing among different pedestrians and inaccurate pedestrian detection bounding boxes further complicate its practical application.Personal belongings information(e.g.,backpacks,handbags) is often overlooked by semantic models because these items are not person body parts,but personal belongings information provide crucial contextual information for re-identification.On the other hand,attribute descriptions,such as gender,type of upper body clothes,color of upper body clothes,are also discriminative information in person re-identification and can effectively enhance Person Re-ID task performance.Addressing the issues that current semantic methods cannot effectively extract potential personal belongings information and clustering methods are too coarse,failing to fully utilize the attribute information of local semantic features,this paper proposed a pedestrian re-identification algorithm based on fine-grained local semantics and attribute learning.This algorithm extracts information about personal belongings and obtains attribute descriptions from semantic regions.The proposed method involves several key modules.Firstly,the Fine-grained Local Semantics(FLS) Module integrates the personal belonging regions generated by the feature clustering method into the semantic parsing results generated by an additional semantic model,addressing the problem of many additional semantic parsing models missing personal belongings information and resulting in smooth and more comprehensive semantic regions.Secondly,Attribute Learning Module(ALM) uses the g

关 键 词：语义分析 属性预测 相关性 行人重识别 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]