Attributes-based person re-identification via CNNs with coupled clusters loss  

作　　者：SUN Rui HUANG Qiheng FANGWei ZHANG Xudong 

机构地区：[1]Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education),Hefei University of Technology,Hefei 230601,China [2]School of Computer and Information,Hefei University of Technology,Hefei 230601,China

出　　处：《Journal of Systems Engineering and Electronics》2020年第1期45-55,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61471154;61876057);the Fundamental Research Funds for Central Universities(JZ2018YYPY0287)

摘　　要：Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.Person re-identification(re-id) involves matching a person across nonoverlapping views, with different poses, illuminations and conditions. Visual attributes are understandable semantic information to help improve the issues including illumination changes, viewpoint variations and occlusions. This paper proposes an end-to-end framework of deep learning for attribute-based person re-id. In the feature representation stage of framework, the improved convolutional neural network(CNN) model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features. Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model. The coupled clusters loss function is used in the training stage of the framework, which enhances the discriminability of both types of features. The combined features are mapped into the Euclidean space. The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same. Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

关 键 词：person re-identification(re-id) convolutions neural network(CNN) attributes coupled clusters loss(CCL) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]