基于显著性多尺度特征协作融合的行人重识别方法

doi:10.19678/j.issn.1000-3428.0057938

摘要/Abstract

摘要： 由于背景信息复杂、遮挡等因素的影响，现有基于局部特征的行人重识别方法所提取的特征不具有辨别力和鲁棒性，从而导致重识别精度较低，针对该问题，提出一种基于显著性检测与多尺度特征协作融合的SMC-ReID方法。利用显著性检测提取行人中具有判别力的特征区域，融合显著性特征与全局特征并完成不同尺度的切块，将上述不同尺度的特征进行协作融合以保证特征切块后的连续性，根据全局特征和局部特征的差异性联合3种损失函数进行学习。在推理阶段，将各个尺度的特征降低到同一维度并融合成新的特征向量，以实现相似性度量。在行人重识别公开数据集Market1501、DukeMTMC-reID和CUHK03上进行实验，结果表明，SMC-ReID方法所提取的特征具有较强的可区分性和鲁棒性，识别准确率优于SVDNet和PSE+ECN等方法。

关键词: 显著性检测, 多尺度特征, 协作融合, 多损失联合学习, 行人重识别, 深度学习

Abstract: The existing person re-identification methods are limited by multiple factors, such as complex background information and occlusion, which reduces the discrimination and robustness of extracted features, leading to a low re-identification accuracy.To address the problem, this paper proposes a new method called SMC-ReID based on saliency detection and collaborative fusion of multi-scale features.The method employs saliency detection to extract discriminative feature areas in pedestrians, and the saliency features are fused with global features.Then the features are cut at different scales, and collaboratively fused to ensure the continuity of the cut features.Finally, the three loss functions are combined to learn based on the differences between global and local features.In the inference stage, the features of each scale are reduced to the same dimension, and fused into new feature vectors for similarity measurement. Experimental results on the public datasets for person re-identification, such as Market1501, DukeMTMC-reID and CUHK03, show that the features extracted by the proposed method have strong distinguishability and robustness, and the method has higher identification accuracy than SVDNet, PSE+ECN and other advanced algorithms.

Key words: saliency detection, multi-scale feature, joint fusion, multi-loss joint learning, person re-identification, deep learning

中图分类号:

TP311

董亚超, 刘宏哲, 徐成. 基于显著性多尺度特征协作融合的行人重识别方法[J]. 计算机工程, 2021, 47(6): 234-244,252.

DONG Yachao, LIU Hongzhe, XU Cheng. Person Re-Identification Method Based on Joint Fusion of Saliency Multi-Scale Features[J]. Computer Engineering, 2021, 47(6): 234-244,252.

https://www.ecice06.com/CN/Y2021/V47/I6/234

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