计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (3): 661-668.DOI: 10.3778/j.issn.1673-9418.2010046
收稿日期:
2020-09-14
修回日期:
2020-11-16
出版日期:
2022-03-01
发布日期:
2020-12-08
通讯作者:
+ E-mail: j-li@cauc.edu.cn作者简介:
李杰(1984—),男,山西原平人,硕士,工程师,主要研究方向为行人再识别、数据挖掘等。
基金资助:
Received:
2020-09-14
Revised:
2020-11-16
Online:
2022-03-01
Published:
2020-12-08
About author:
LI Jie, born in 1984, M.S., engineer. His research interests include person re-identification, data mining, etc.
Supported by:
摘要:
针对现有行人再识别算法在处理图像分辨率低、光照差异、姿态和视角多样等情况时,准确率低的问题,提出了基于空间注意力和纹理特征增强的多任务行人再识别算法。算法设计的空间注意力模块更注重与行人属性相关的潜在图像区域,融入属性识别网络,实现属性特征的挖掘;提出的行人再识别网络的纹理特征增强模块通过融合不同空间级别所对应的全局和局部特征,减弱了光照、遮挡等对行人再识别的干扰;最后通过多任务加权损失函数将属性特征和行人特征巧妙融合,避免了由属性异质性造成的再识别精度损失。实验结果表明,该方法在Market-1501和DukeMTMC-reID两大主流行人再识别数据集上的平均精度分别可以达到81.1%和70.1%。
中图分类号:
李杰. 结合注意力和纹理特征增强的行人再识别[J]. 计算机科学与探索, 2022, 16(3): 661-668.
LI Jie. Attention and Texture Feature Enhancement for Person Re-identification[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(3): 661-668.
方法 | gender | hat | boots | L.up | B.pack | H.bag | Bag | C.shoes | C.up | C.low | mAP |
---|---|---|---|---|---|---|---|---|---|---|---|
不包含空间注意力模块 | 69.4 | 79.4 | 78.5 | 87.6 | 67.7 | 89.8 | 83.2 | 88.6 | 80.4 | 90.1 | 81.5 |
包含空间注意力模块 | 86.3 | 89.9 | 90.7 | 88.4 | 83.4 | 93.5 | 81.0 | 91.2 | 94.3 | 91.4 | 89.0 |
表1 空间注意力模块对属性识别的有效性验证
Table 1 Validity verification of spatial attention module for attribute recognition %
方法 | gender | hat | boots | L.up | B.pack | H.bag | Bag | C.shoes | C.up | C.low | mAP |
---|---|---|---|---|---|---|---|---|---|---|---|
不包含空间注意力模块 | 69.4 | 79.4 | 78.5 | 87.6 | 67.7 | 89.8 | 83.2 | 88.6 | 80.4 | 90.1 | 81.5 |
包含空间注意力模块 | 86.3 | 89.9 | 90.7 | 88.4 | 83.4 | 93.5 | 81.0 | 91.2 | 94.3 | 91.4 | 89.0 |
λ | Market-1501 | DukeMTMC | ||
---|---|---|---|---|
Rank-1/% | mAP/% | Rank-1/% | mAP/% | |
2 | 93.8 | 80.3 | 84.4 | 70.2 |
4 | 94.2 | 81.1 | 84.7 | 70.1 |
6 | 94.2 | 80.8 | 83.6 | 69.1 |
8 | 93.7 | 80.3 | 84.1 | 68.8 |
表2 不同 λ值在数据集上的结果
Table 2 Results of different λon datasets
λ | Market-1501 | DukeMTMC | ||
---|---|---|---|---|
Rank-1/% | mAP/% | Rank-1/% | mAP/% | |
2 | 93.8 | 80.3 | 84.4 | 70.2 |
4 | 94.2 | 81.1 | 84.7 | 70.1 |
6 | 94.2 | 80.8 | 83.6 | 69.1 |
8 | 93.7 | 80.3 | 84.1 | 68.8 |
实验 | 方法 | Market-1501 | DukeMTMC-reID | ||
---|---|---|---|---|---|
Rank-1/% | mAP/% | Rank-1/% | mAP/% | ||
1 | 不包含空间注意力模块的属性识别网络 | 92.4 | 79.1 | 82.6 | 67.9 |
2 | 不包含纹理特征增强模块的行人再识别网络 | 92.3 | 78.4 | 78.0 | 66.1 |
3 | 单任务属性识别网络 | 74.5 | 50.2 | 61.1 | 38.0 |
4 | 单任务行人再识别网络 | 91.6 | 77.3 | 81.1 | 65.1 |
5 | 本文方法 | 94.2 | 81.1 | 84.7 | 70.1 |
表3 Market-1501和DukeMTMC-reID评测集结果对比
Table 3 Results comparison of Market-1501 and DukeMTMC-reID evaluation sets
实验 | 方法 | Market-1501 | DukeMTMC-reID | ||
---|---|---|---|---|---|
Rank-1/% | mAP/% | Rank-1/% | mAP/% | ||
1 | 不包含空间注意力模块的属性识别网络 | 92.4 | 79.1 | 82.6 | 67.9 |
2 | 不包含纹理特征增强模块的行人再识别网络 | 92.3 | 78.4 | 78.0 | 66.1 |
3 | 单任务属性识别网络 | 74.5 | 50.2 | 61.1 | 38.0 |
4 | 单任务行人再识别网络 | 91.6 | 77.3 | 81.1 | 65.1 |
5 | 本文方法 | 94.2 | 81.1 | 84.7 | 70.1 |
实验 | 方法 | Market-1501 | DukeMTMC-reID | ||
---|---|---|---|---|---|
Rank-1/% | mAP/% | Rank-1/% | mAP/% | ||
单任务行人再识别算法 | SVDNet[ | 82.3 | 62.1 | 76.7 | 56.8 |
PAN[ | 82.8 | 63.4 | 71.6 | 51.5 | |
PAR[ | 81.0 | 63.4 | — | — | |
MultiLoss[ | 83.9 | 64.4 | — | — | |
TripletLoss[ | 84.9 | 69.1 | — | — | |
MultiScale[ | 88.9 | 73.1 | 79.2 | 60.6 | |
PCB[ | 92.4 | 77.3 | 81.8 | 66.1 | |
HA-CNN[ | 91.2 | 75.7 | 80.5 | 63.8 | |
多任务行人再识别算法 | APR[ | 84.3 | 64.7 | 70.7 | 51.9 |
ACRN[ | 83.6 | 62.6 | 72.6 | 52.0 | |
JCM[ | 91.3 | 81.2 | — | — | |
CA3Net[ | 93.2 | 80.0 | 84.6 | 70.2 | |
本文算法 | Proposed | 94.2 | 81.1 | 84.7 | 70.1 |
表4 与现有方法在Market-1501和DukeMTMC-reID数据集上的结果对比
Table 4 Comparison with existing methods on Market-1501 and DukeMTMC-reID datasets
实验 | 方法 | Market-1501 | DukeMTMC-reID | ||
---|---|---|---|---|---|
Rank-1/% | mAP/% | Rank-1/% | mAP/% | ||
单任务行人再识别算法 | SVDNet[ | 82.3 | 62.1 | 76.7 | 56.8 |
PAN[ | 82.8 | 63.4 | 71.6 | 51.5 | |
PAR[ | 81.0 | 63.4 | — | — | |
MultiLoss[ | 83.9 | 64.4 | — | — | |
TripletLoss[ | 84.9 | 69.1 | — | — | |
MultiScale[ | 88.9 | 73.1 | 79.2 | 60.6 | |
PCB[ | 92.4 | 77.3 | 81.8 | 66.1 | |
HA-CNN[ | 91.2 | 75.7 | 80.5 | 63.8 | |
多任务行人再识别算法 | APR[ | 84.3 | 64.7 | 70.7 | 51.9 |
ACRN[ | 83.6 | 62.6 | 72.6 | 52.0 | |
JCM[ | 91.3 | 81.2 | — | — | |
CA3Net[ | 93.2 | 80.0 | 84.6 | 70.2 | |
本文算法 | Proposed | 94.2 | 81.1 | 84.7 | 70.1 |
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