一种基于改进三元组损失和特征融合的行人重识别方法

doi:10.11896/jsjkx.200800200

摘要/Abstract

摘要： 行人重识别旨在跨摄像头条件下,从目标数据库中检索出特定的行人目标,其在视频监控领域有重要的应用价值。目前其研究难点为样本图像类内差异大、类间差异小,因此如何设计并训练深度神经网络对行人图片提取一个判别力更强的特征成为了其关键。针对以往研究只单独进行全局特征或局部特征学习的不足,提出了一种联合全局特征和局部特征学习的网络结构,该结构能够同时提取全局特征和具有较强区分力的局部细节特征;针对每部分局部特征对行人特征描述的重要性不同,文中提出了一种局部特征的融合方式,该方法能够自适应地生成各个局部特征的权重,最后将融合后的局部特征和全局特征结合使行人特征得到更全面的表征;另外,针对以往的基于难样本挖掘的三元组损失具有优化目标模糊的特点,提出了一种改进的基于难样本挖掘的三元组损失函数。文中分别在行人重识别主流数据集Market-1501和DukeMTMC-reID上验证了所提方法的有效性,其mAP值分别达到了82.16%和74.02%,Rank-1值分别达到了92.75%和86.8%。

关键词: 检索, 三元组损失, 深度学习, 特征融合, 行人重识别

Abstract: Person re-identification aims to retrieve specific pedestrian targets from the target database under the condition of cross camera.It has important application value in the field of video surveillance.At present,the difficulty of the research is that the sample images have large intra class differences and small inter class differences.Therefore,how to design and train the deep neural network to extract a more discriminative feature from pedestrian images is the key.In this paper,we propose a network structure combining global features and local features learning,which can extract global features and local features simultaneously.In view of the different importance of each part of the local features to the description of pedestrian features,this paper proposes a fusion method of local features,which can adaptively generate the weight of each local feature.Finally,the local features and glo-bal features are combined to make the pedestrian features get more comprehensive representation.In addition,in view of the fuzzy optimization objective of the previous triple loss based on hard sample mining,this paper proposes an improved triple loss function based on hard sample mining.The effectiveness of the proposed method is verified on the mainstream person re-identification data sets Market-1501 and DukeMTMC-reID,respectively,and the mAP values are 82.16% and 74.02%,and the Rank-1 values are 92.75% and 86.8%,respectively.

Key words: Deep learning, Feature fusion, Person re-identification, Retrieval, Triplet loss

中图分类号:

TP391.4

张新峰, 宋博. 一种基于改进三元组损失和特征融合的行人重识别方法[J]. 计算机科学, 2021, 48(9): 146-152. https://doi.org/10.11896/jsjkx.200800200

ZHANG Xin-feng, SONG Bo. A Person Re-identification Method Based on Improved Triple Loss and Feature Fusion[J]. Computer Science, 2021, 48(9): 146-152. https://doi.org/10.11896/jsjkx.200800200

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