基于注意力机制与条件卷积的行人重识别方法

姬广凯; 王蓉; 彭舒凡

doi:10.13700/j.bh.1001-5965.2022.0454

基于注意力机制与条件卷积的行人重识别方法

doi: 10.13700/j.bh.1001-5965.2022.0454

中国人民公安大学信息网络安全学院，北京 100038

基金项目: 国家自然科学基金(62076246)

详细信息

通讯作者:
E-mail： dbdxwangrong@163.com

中图分类号: V221⁺.3；TB553
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出版历程
- 收稿日期: 2022-06-02
- 录用日期: 2022-10-06
- 网络出版日期: 2022-10-10
- 整期出版日期: 2024-02-27

Person re-identification method based on attention mechanism and CondConv

Police Information Engineering and Network Secuvity College, People’s Public Security University of China，Beijing 100038，China

Funds: National Natural Science Foundation of China (62076246)

More Information

Corresponding author: E-mail： dbdxwangrong@163.com

摘要

摘要:
行人重识别是计算机视觉领域的一个重要部分，但是容易受到行人图片实际采集环境的影响，导致行人特征表达不充分，进一步导致模型精度不高。提出一种基于注意力机制和条件卷积改进的行人重识别方法，使行人特征得到更充分的表达。将注意力机制引入特征提取网络ResNet50中，对输入图像空间和通道上的关键信息进行加权强化，同时抑制可能的噪声；将条件卷积模块引入主干网络，动态调整卷积核参数，使模型能够在保持高效推理的同时提高容量和性能；利用 Market1501、MSMT17和DukeMTMC-ReID主流数据集对改进方法进行评估，Rank1分别提升1.1%、2.4%、1.3%，mAP分别提升0.5%、2.3%、1.3%，结果表明：改进方法能够使行人特征得到更好的表达，识别精度得到提升。
- 注意力机制 /
- 条件卷积 /
- ResNet50 /
- 行人重识别 /
- 深度学习
Abstract:
Person Re-identification is an important part of the field of computer vision, but it is easily affected by the actual collection environment of person images, resulting in insufficient expression of person features and further leading to low model accuracy. An improved person re-identification method based on attention mechanism and CondConv is proposed to fully express pedestrian features. The attention mechanism is introduced into the feature extraction network ResNet50, and the key information in the input image space and channel is weighted, while suppressing possible noise. The CondConv is introduced into the backbone network and the convolution kernel parameters are dynamically adjusted to improve the capacity and performance of the model while maintaining efficient reasoning. Mainstream data sets such as Market1501, MSMT17 and DukeMTMC-ReID are used to evaluate the improved method. Rank-1 is increased by 1.1%, 2.4% and 1.3% respectively, and mAP is increased by 0.5%, 2.3% and 1.3%; respectively. The results show that the improved method can better express person features and improve recognition accuracy.
- attention mechanism /
- CondConv /
- ResNet50 /
- person re-identification /
- deep learning

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图 1 CTL 网络模型

Figure 1. CTL network model

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图 2 改进后的CTL网络模型

Figure 2. Improved CTL network model

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图 3 CBAM-ResNet50网络结构

Figure 3. CBAM-ResNet50 network structure

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图 4 CBAM 结构

Figure 4. Structure of CBAM

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图 5 条件卷积模块结构图

Figure 5. Structure of CondConv model

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图 6 数据集Market1501^[13]上的CMC曲线对比

Figure 6. CMC curve comparison chart on dataset Market1501^[13]

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图 7 数据集MSMT17^[14]上的CMC曲线对比

Figure 7. CMC curve comparison chart on dataset MSMT17^[14]

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图 8 数据集DukeMTMC-ReID^[15]上的CMC曲线对比

Figure 8. CMC curve comparison chart on dataset DukeMTMC-ReID^[15]

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表 1 引入条件卷积前后参数量与计算量对比

Table 1. Comparison of number of parameters and flops before and after introduction of CondConv

模块	参数量	计算量
Basic Layer3	7098368	959119360
CondConv Layer3	3564050	506139136
Basic Layer4	14964736	529301504
CondConv Layer4	7891465	302813696

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表 2 数据集简介

Table 2. Introduction of datasets

数据集	公开年份	摄像机数/个	ID数	图片数量/张	评测
Market1501^[13]	2015	6	1501	32668	CMC+mAP
MSMT17^[14]	2018	15	4101	126441	CMC+mAP
DukeMTMC-ReID^[15]	2017	8	1404	36411	CMC+mAP

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表 3 基于空间通道注意力机制改进后的CTL模型在不同数据集上的性能对比

Table 3. Performance comparison of improved CTL model based on CBAM attention mechanism on different datasets %

模型	Rank-1			mAP
模型	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]
CTL	97.5	89.5	95.4	98.3	91.3	96.1
引入注意力机制后	98.3	91.0	96.5	98.6	92.9	97.0

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表 4 基于条件卷积模块改进后的CTL模型在不同数据集上的性能对比

Table 4. Performance comparison of improved CTL model based on CondConv module on different datasets %

模型	Rank-1			mAP
模型	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]
CTL	97.5	89.5	95.4	98.3	91.3	96.1
引入条件卷积后	98.5	90.6	96.2	98.7	92.6	96.8

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表 5 基于注意力机制和条件卷积改进后的CTL模型在不同数据集上的性能对比

Table 5. Performance comparison of improved CTL model based on attention mechanism and CondConv on different datasets %

模型	Rank-1			mAP
模型	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]
CTL	97.5	89.5	95.4	98.3	91.3	96.1
同时引入注意力机制和条件卷积后	98.6	91.9	96.7	98.8	93.6	97.4

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表 6 改进方法与最新方法的性能比较

Table 6. Performance comparison between improved method and latest methods %

方法	Rank-1			mAP
方法	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]	Market1501^[13]	MSMT17^[14]	DukeMTMC-ReID^[15]
CDNet^[4]	95.1	78.9	88.6	86.0	54.7	76.8
OSNet^[6]	94.8	79.1	88.7	86.7	55.1	76.6
PAT^[16]	95.4		88.8	88.0		78.2
HLGAT^[17]	97.5		92.7	93.4		87.3
CTL	97.5	89.5	95.4	98.3	91.3	96.1
CTL +CBAM	98.3	91.0	96.5	98.6	92.9	97.0
CTL +CondConv	98.5	90.6	96.2	98.7	92.6	96.8
CTL+CBAM+CondConv	98.6	91.9	96.7	98.8	93.6	97.4

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