基于双向关系相似度函数学习的行人再识别  

Learning Bidirectional Relationship Similarity Function for Person Re-Identification

作　　者：张娜[1] 张福星 王强[1] 胡玲玲 桂江生[1] ZHANG Na;ZHANG Fu-Xing;WANG Qiang;HU Ling-Ling;GUI Jiang-Sheng(School of Information and Electronics, Zhejiang Sci-Tech University, Hangzhou 310018, China)

机构地区：[1]浙江理工大学信息学院,杭州310018

出　　处：《计算机系统应用》2018年第5期33-40,共8页Computer Systems & Applications

基　　金：国家自然科学基金(61379036;61502430);国家自然科学基金委中丹合作项目(61361136002);浙江省重大科技专项重点工业项目(2014C01047);浙江理工大学521人才培养计划(20150428)

摘　　要：当前的行人再识别在度量学习上采用马氏距离相似度函数,该相似度函数只与特征差分空间有关,忽略了一对行人图像中每个个体的外观特征,针对上述问题,提出了通过学习一个双向关系相似度函数(Bidirectional Relationship Similarity Function,BRSF),来计算一对行人图像的相似度.BRSF不但描述了一对行人图像的互相关关系,而且关联了一对行人图像的自相关关系.该文利用KISSME(Keep It Simple and Straightforward Metric)算法的思想进行相似度函数学习,把一对样本特征的自相关关系和互相关关系用高斯分布来表示,通过把最终高斯分布的比值转换为BRSF的形式,得到一个对背景、视角、姿势的变化具有鲁棒性的相似度函数.在VIPe R,QMUL GRID两个行人再识别数据集上的实验结果表明,本文算法具有较高的识别率,其中在VIPe R数据集上,Rank1达到了53.21%.These dominant algorithms to learn a similarity is the metric learning that learns a Mahalanobis Similarity Function（MSF） to estimate the similarity of a pair of persons. However, the MSF only projects a pair of persons into feature difference space and ignores the appearance of each individual. In this study, we proposed to learn a Bidirectional Relationship Similarity Function（BRSF） that greatly strengthens the modeling ability of the similarity function. BRSF not only represents the cross correlation relationship of a pair of persons, but also describes the auto correlation relationship.We use the ideal of the Keep It Simple Straightforward Metric（KISSME） algorithm to learn a similarity function.Specifically, the auto correlation relationship and cross correlation relationship of a pair of sample features are expressed by Gaussian distribution. Finally, by converting the ratio of the final Gaussian distribution into the form of BRSF, we get a similarity function which is robust to the change of background, viewpoint, and posture. The proposed method is demonstrated on two public benchmark datasets including VIPe R and QMUL GRID, and experimental results show that the proposed method achieves excellent re-identification rates compared with other similar algorithms. Moreover, the reidentification results on the VIPe R dataset with half of dataset sampled as training samples are quantitatively analyzed,and the performance of the proposed method achieves a 53.21% at Rank1（represents the correct matched pair）.

关 键 词：行人再识别 距离度量学习 双向关系相似度函数 滑动分块 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]