关键细粒度信息指导的多尺度遮挡行人重识别

周玉; 赵小锋; 汪一; 孙彦景; 李松

doi:10.11999/JEIT230686

关键细粒度信息指导的多尺度遮挡行人重识别

doi: 10.11999/JEIT230686

周玉^{1, 3},
赵小锋¹,
汪一^{1, 2, ,},
孙彦景¹,
李松¹

1.
中国矿业大学信息与控制工程学院徐州 221116
2.
江苏师范大学科文学院徐州 221132
3.
徐州市第一人民医院徐州 221116

基金项目: 国家自然科学基金(62001475)，江苏省自然科学基金(BK20200649)

详细信息

作者简介:
周玉：女，副教授，研究方向为人工智能、图像处理、行人重识别

赵小锋：男，硕士生，研究方向为图像处理、遮挡行人重识别

汪一：男，博士生，研究方向为多媒体图像处理、行人重识别

孙彦景：男，教授，研究方向为图像处理、行人重识别

李松：男，副教授，研究方向为图像处理、行人重识别

通讯作者:
汪一　ywang@cumt.edu.cn

中图分类号: TN911.73; TP391.41
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- 文章访问数: 476
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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-07
- 修回日期: 2024-01-19
- 网络出版日期: 2024-01-26
- 刊出日期: 2024-06-30

Multi-Scale Occluded Person Re-Identification Guided by Key Fine-Grained Information

ZHOU Yu^{1, 3},
ZHAO Xiaofeng¹,
WANG Yi^{1, 2
, ,},
SUN Yanjing¹,
LI Song¹

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
Kewen College, Jiangsu Normal University, Xuzhou 221132, China
3.
Xuzhou First Peoples Hospital, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62001475), The National Natural Science Foundation of Jiangsu Province(BK20200649)

摘要

摘要: 为了减轻背景和遮挡等干扰信息对行人身份重识别(ReID)准确率的影响以及充分利用细粒度和粗粒度信息之间的互补性，该文提出关键细粒度信息指导的多尺度遮挡行人重识别网络。首先，将图像划分为两种不同尺寸的重叠图像块，构建同时包含细粒度和粗粒度信息提取分支的多尺度识别网络，以更好模拟人类观察图像时的多尺度特性以及观察相邻区域时的连续性特性。然后，考虑到细粒度分支能够提取更多的图像细节信息且细粒度和粗粒度信息之间存在一定的共性与差异，进一步通过细粒度注意力模块实现细粒度信息对粗粒度信息学习分支的指导。其中，参与指导的细粒度信息是通过干扰信息剔除(IIE)模块滤除干扰信息后保留的关键信息。最后，通过双次差分获取与行人身份识别相关的关键信息，并通过标签和特征等多维度的联合监督，实现行人身份的预测。在多个公开的行人重识别数据库进行的大量实验证明了该算法的性能优越性以及其中各个模块的有效性和必要性。
- 遮挡行人重识别 /
- 多尺度 /
- 细粒度信息 /
- 粗粒度信息 /
- 干扰信息剔除
Abstract: To reduce the influence of background and occlusion on the accuracy of pedestrian identity Re-IDentification (ReID) and make full use of the complementarity between fine-grained and coarse-grained information, a multi-scale occluded pedestrian ReID network guided by key fine-grained information is proposed. First, the image is divided into two types of overlapping patches with different sizes to better simulate the multi-scale characteristics of human observing images and the continuity characteristics of human observing adjacent regions, so a multi-scale recognition network containing both fine-grained and coarse-grained information extraction branches is constructed. Then, considering fine-grained information contains more details and there are similarities and differences between fine-grained and coarse-grained information, fine-grained attention module is further employed to realize the guide of the fine-grained branch to the coarse-grained branch. Among them, the fine-grained information is the key information retained after filtering out the interference information by the Interference Information Elimination (IIE) module. Finally, the key information related to pedestrian ReID is obtained by bivariate difference, and the prediction of pedestrian identity is realized by multi-dimensional joint supervision such as tags and features. Extensive experiments on several public pedestrian ReID databases prove the superiority of this algorithm and the effectiveness and necessity of each module.
- Occluded person ReID /
- Multi-scale /
- Fine-grained information /
- Coarse-grained information /
- Interference information elimination

HTML全文

图 1 本文算法的结构框图

下载: 全尺寸图片幻灯片

图 2 干扰信息剔除模块结构图

下载: 全尺寸图片幻灯片

图 3 细粒度信息指导编码模块结构图

下载: 全尺寸图片幻灯片

图 4 参数$ \alpha $和$ \eta $对算法性能的影响

下载: 全尺寸图片幻灯片

图 5 特征图可视化结果

下载: 全尺寸图片幻灯片

表 1 本文算法及对比算法在Occluded-Duke数据集上的实验结果(%)

性能	PCB^[5]	PGFA^[8]	PVPM ^[10]	ISP ^[20]	HOReID ^[11]	MoS^[4]	PAT ^[21]	TransReID* ^[14]	PFD ^[5]	本文算法
Rank-1	42.6	51.4	47.0	62.8	55.1	61.0	64.5	66.4	69.5	71.2
mAP	33.7	37.3	37.7	52.3	43.8	49.2	53.6	59.2	61.8	62.3

下载: 导出CSV

表 2 本文算法及对比算法在Occluded- REID数据集上的实验结果(%)

性能	PCB^[5]	PVPM ^[10]	HOReID^[11]	PAT ^[21]	PFD ^[5]	DSR^[22]	Yang^[6]	Yan^[23]	本文算法
Rank-1	41.3	66.8	80.3	81.6	81.5	72.8	81.0	78.5	86.3
mAP	38.9	59.5	70.2	72.1	83.0	62.8	71.0	72.9	81.3

下载: 导出CSV

表 3 本文算法及对比算法在全身数据集Market-1501和DukeMTMC上的实验结果(%)

方法	Market-1501		DukeMTMC
方法	Rank-1	mAP	Rank-1	mAP
PCB ^[5]	92.3	71.4	81.8	66.1
PGFA^[8]	91.2	76.8	82.6	65.5
ISP ^[20]	95.3	88.6	89.6	80.0
HOReID ^[11]	94.2	84.9	86.9	75.6
MoS ^[4]	95.4	89.0	90.6	80.2
PAT ^[21]	95.4	88.0	88.8	78.2
TransReID* ^[14]	95.2	88.9	90.7	82.0
PFD ^[5]	95.5	89.7	91.2	83.2
本文算法	95.7	89.5	90.7	82.5

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表 4 本文算法中各模块的贡献(%)

方法	Rank-1	Rank-5	Rank-10	mAP
Baseline	61.9	78.2	83.8	53.1
Baseline +IIE	65.1	80.3	85.4	55.6
无FGI	65.7	80.0	84.6	58.0
无DD	64.2	80.2	85.1	57.0
粗粒度->细粒度	69.1	82.7	86.7	61.5
粗粒度<->细粒度	48.4	66.9	73.6	43.0
本文算法	71.2	82.9	86.9	62.3

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表 5 超参数$ {\omega _1} $和$ {\omega _2} $对算法性能Rank-1(mAP)的影响(%)

	0	1	2	3
0	67.1(56.5)	67.1(56.8)	67.3(57.1)	67.9(58.5)
1	66.4(56.4)	68.5(58.1)	68.5(57.9)	67.3(57.4)
2	67.3(57.5)	67.9(57.2)	70.2(62.1)	68.7(58.9)
3	66.6(56.5)	67.6(57.4)	68.6(58.7)	71.2(62.3)

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表 6 图像块尺寸对算法性能的影响(%)

	4$ \times $4, 8$ \times $8	4$ \times $4, 12$ \times $12	4$ \times $4, 16$ \times $16	4$ \times $4, 20$ \times $20	8$ \times $8, 12$ \times $12	8$ \times $8, 16$ \times $16	8$ \times $8, 20$ \times $20	12$ \times $12, 16$ \times $16	12$ \times $12, 20$ \times $20	16$ \times $16, 20$ \times $20
Rank-1	63.3	66.4	64.3	64.8	69.3	68.7	70.2	71.2	69.6	66.5
mAP	52.9	55.7	53.8	55.4	59.9	59.1	60.2	62.3	59.4	57.2

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表 7 3种尺度输入对算法性能的影响(%)

	8$ \times $8, 12$ \times $12, 16$ \times $16	8$ \times $8, 12$ \times $12,20$ \times $20	8$ \times $8, 16$ \times $16,20$ \times $20	12$ \times $12, 16$ \times $16, 20$ \times $20
Rank-1	64	65	66.7	67.3
mAP	53.8	54.3	56	57.4
参数量 (M)	313.47	313.47	313.47	313.47
响应时间(s)	12.57	12.37	12.08	10.25

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参考文献(23)

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