基于自适应融合网络的跨域行人重识别方法

郭迎春; 冯放; 阎刚; 郝小可

doi:10.16383/j.aas.c220083

基于自适应融合网络的跨域行人重识别方法

doi: 10.16383/j.aas.c220083

郭迎春^1,,
冯放^1,,
阎刚^1,,
郝小可^1,

1.
河北工业大学人工智能与数据科学学院天津 300401

基金项目: 国家自然科学基金(60302018, 61806071, 62102129), 河北省自然科学基金(F2019202381, F2019202464)资助

详细信息

作者简介:
郭迎春：河北工业大学人工智能与数据科学学院副教授. 2006年获得天津大学信号与信息处理专业博士学位. 主要研究方向为数字图像处理和计算机视觉. E-mail: gyc@scse.hebut.edu.cn

冯放：河北工业大学人工智能与数据科学学院硕士研究生. 主要研究方向为行人重识别. E-mail: fengfang0901@163.com

阎刚：河北工业大学人工智能与数据科学学院副教授. 2019年获得河北工业大学微电子学与固体电子学专业博士学位. 主要研究方向为图像处理和智能监控. 本文通信作者. E-mail: yangang@scse.hebut.edu.cn

郝小可：河北工业大学人工智能与数据科学学院副教授. 2017年获得南京信息工程大学计算机与技术专业博士学位. 主要研究方向为机器学习, 医学图像分析和生物信息学. E-mail: haoxiaoke@scse.hebut.edu.cn

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出版历程
- 收稿日期: 2022-01-28
- 录用日期: 2022-07-30
- 网络出版日期: 2022-08-29
- 刊出日期: 2022-11-22

Cross-domain Person Re-identification on Adaptive Fusion Network

1.
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401

Funds: Supported by National Natural Science Foundation of China (60302018, 61806071, 62102129) and Natural Science Foundation of Hebei Province (F2019202381, F2019202464)

More Information

Author Bio:
GUO Ying-Chun　Associate professor at the School of Artificial Intelligence, Hebei University of Technology. She received her Ph.D. degree in signal and information processing from Tianjin University in 2006. Her research interest covers digital image processing and computer vision

FENG Fang　Master student at the School of Artificial Intelligence, Hebei University of Technology. His main research interest is person re-identification

YAN Gang　Associate professor at the School of Artificial Intelligence, Hebei University of Technology. He received his Ph.D. degree in microelectronics and solid state electronics from Hebei University of Technology in 2019. His research interest covers image processing and intelligent surveillance. Corresponding author of this paper

HAO Xiao-Ke　Associate professor at the School of Artificial Intelligence, Hebei University of Technology. He received his Ph.D. degree in computer science and technology from Nanjing University of Information Science and Technology in 2017. His research interest covers machine learning, medical image analysis and bioinformatics

摘要

摘要: 无监督跨域的行人重识别旨在将从有标签的源域中学习到的知识迁移到无标签的目标域, 具有实用性和有效性而得到广泛关注. 基于聚类的跨域行人重识别可以生成伪标签并对模型进行优化使得其表现较其他方法更优, 然而这类方法由于过于依赖聚类伪标签的准确性, 忽略了对伪标签噪声的处理, 导致噪声随着网络迭代而不断扩大, 影响模型的鲁棒性. 针对这个问题, 提出了基于自适应融合网络的方法, 利用双网络结构共同学习, 并将学习到的知识进行融合得到融合网络; 为了区分两个网络的学习能力, 设计了自适应融合策略; 同时, 利用细粒度风格转换模块对目标域数据集进行处理, 降低行人图像对相机变换的敏感度. 在行人重识别基准数据集Market1501、DukeMTMC-ReID和MSMT17上, 通过评估指标平均精度均值和Rank-n与主流的方法进行了对比实验, 验证了该方法的有效性.
- 跨域行人重识别 /
- 自适应融合网络 /
- 细粒度风格转换 /
- 深度学习
Abstract: Unsupervised cross-domain person re-identification aims to transfer the knowledge learned from labeled source domain to unlabeled target domain, which has attracted wide attention due to its practicability and effectiveness. Cross-domain person re-identification based on clustering can generate pseudo-labels and optimize the model to make its performance better than other methods. However, these methods rely too much on the accuracy of clustering pseudo labels and ignore to deal with pseudo-label noise, which leads to the continuous expansion of noise with network iteration and affects the robustness of the models. To address this problem, this paper proposes a method based on fine-grained style transfer and adaptive fusion network, which uses dual network structure to learn together and fuse the learned knowledge to obtain a fusion network. To treat the learning ability of the two networks differently, an adaptive fusion strategy is designed based on the different weights of the two networks in each fusion process. At the same time, a fine-grained style transfer module is used to process the target domain dataset, thereby reducing the sensitivity of person images to camera transformation. On the person re-identification benchmark datasets Market1501, DukeMTMC-ReID and MSMT17, the effectiveness of the proposed method was verified by comparing mean average precision and Rank-n with the state-of-the-art methods.
- Cross-domain person re-identification /
- adaptive fusion network /
- fine-grained style transfer /
- deep learning

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图 1 自适应融合网络模型

Fig. 1 Adaptive fusion network model

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图 2 细粒度风格转换模块

Fig. 2 Fine-grained style conversion module

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图 3 $ \lambda_{id}$取值评估实验

Fig. 3 Evaluation of different values of $ \lambda_{id}$

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图 4 $ \lambda_{t r i}$取值评估实验

Fig. 4 Evaluation of different values of $ \lambda_{t r i}$

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图 5 聚类数量取值评估实验

Fig. 5 Evaluation of different numbers of clustering

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表 1 本文的自适应融合网络模型参数量表

Table 1 The model parameter number of the proposed adaptive fusion network

参数	取值
总参数	23512128 × 2
可训练参数	23512128 × 2
参数大小 (MB)	89.69 × 2
估计总大小 (MB)	1199.45 × 2

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表 2 在Market1501和DukeMTMC-ReID上与主流方法比较 (%)

Table 2 Comparison with the state-of-the-art methods on Market1501 and DukeMTMC-ReID (%)

方法	Duke-to-Market				Market-to-Duke
方法	mAP	Rank-1	Rank-5	Rank-10	mAP	Rank-1	Rank-5	Rank-10
BUC^[34]	38.3	66.2	79.6	84.5	27.5	47.4	62.6	68.4
SSL^[35]	37.8	71.7	87.4	37.8	28.6	52.5	63.5	68.9
MMFA^[36]	27.4	56.7	75.0	81.8	24.7	45.3	59.8	66.3
TJ-AIDL^[11]	26.5	58.2	74.8	81.1	23.0	44.3	59.6	65.0
D-MMD^[12]	75.1	89.5	95.6	97.1	62.7	79.3	89.3	92.0
TAL-MIRN^[37]	40.0	73.1	86.3	—	41.3	63.5	76.7	—
ATNet^[13]	25.6	55.7	73.2	79.4	24.9	45.1	59.5	64.2
SPGAN + LMP^[14]	26.7	57.7	75.8	82.4	26.2	46.4	62.3	68.0
HHL^[16]	31.4	62.2	78.8	84.0	27.2	46.9	61.0	66.7
ECN^[17]	43.0	75.1	87.6	91.6	40.4	63.3	75.8	80.4
MAR^[18]	67.7	81.9	87.3	40.0	67.1	79.8	84.2	48.0
UDAP^[38]	53.7	75.8	89.5	93.2	49.0	68.4	80.1	83.5
PCB-PAST^[39]	54.6	78.4	—	—	54.3	72.4	—	—
SSG^[20]	58.3	80.0	90.0	92.4	53.4	73.0	80.6	83.2
AD-Cluster^[21]	68.3	86.7	94.4	96.5	54.1	72.6	82.5	85.5
MMT-500^[23]	71.2	87.7	94.9	96.9	63.1	76.8	88.0	92.2
MEB-Net^[40]	76.0	89.9	96.0	97.5	66.1	79.6	88.3	92.2
SILC^[41]	61.8	80.7	90.1	93.0	50.3	68.5	80.2	85.4
DRDL^[42]	42.7	76.8	88.5	91.6	43.2	65.3	76.9	82.2
PREST^[43]	62.4	82.5	92.1	94.9	56.1	74.4	83.7	85.9
SpCL^[44]	76.7	90.3	96.2	97.7	68.8	82.9	90.1	92.5
MLOL^[45]	70.9	86.6	93.1	95.1	69.8	83.1	90.8	93.0
UNRN^[46]	78.1	91.9	96.1	97.8	69.1	82.0	90.7	93.5
本文方法	79.1	91.8	97.1	98.2	68.5	80.7	90.1	92.6
本文方法 + 不确定性	79.9	92.3	97.4	98.3	69.8	82.1	90.5	93.1

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表 3 在MSMT17上与主流方法比较 (%)

Table 3 Comparison with the state-of-the-art methods on MSMT17 (%)

方法	Duke-to-MSMT17				Market-to-MSMT17
方法	mAP	Rank-1	Rank-5	Rank-10	mAP	Rank-1	Rank-5	Rank-10
ECN^[17]	10.2	30.2	41.5	46.8	8.5	25.3	36.3	42.1
SSG^[20]	13.3	32.2	—	51.2	13.2	31.6	—	49.6
MMT-1500^[23]	23.3	50.1	63.9	69.8	22.9	49.2	63.1	68.8
SILC^[41]	12.6	33.1	45.2	48.0	10.9	27.8	38.1	45.8
TAL-MIRN^[37]	14.2	39.0	51.5	—	11.2	30.9	43.5	—
DRDL^[42]	14.9	42.0	53.7	59.1	14.7	38.6	51.4	57.1
PREST^[43]	18.5	43.8	57.5	63.6	15.9	37.8	51.8	57.8
SpCL^[44]	26.5	53.1	65.8	70.5	25.4	51.6	64.3	69.7
MLOL^[45]	22.4	48.3	60.7	66.1	21.7	46.9	59.4	64.7
UNRN^[46]	26.2	54.9	67.3	70.6	25.3	52.4	64.7	69.7
本文方法	30.2	60.4	73.3	77.9	29.4	59.6	72.8	77.5
本文方法 + 不确定	30.8	61.0	73.9	78.3	30.6	61.0	73.7	78.0

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表 4 在Market1501和DukeMTMC-ReID上的消融实验 (%)

Table 4 Ablation experiments on Market1501 and DukeMTMC-ReID (%)

方法	Duke-to-Market				Market-to-Duke
方法	mAP	Rank-1	Rank-5	Rank-10	mAP	Rank-1	Rank-5	Rank-10
直接转换	31.8	61.9	76.4	82.2	29.9	46.2	61.9	68.0
基线	53.5	76.0	88.1	91.9	48.2	66.4	79.8	84.0
本文方法 w/F	74.3	90.2	95.8	97.6	62.9	77.1	87.9	91.5
本文方法 w/(F + T)	77.6	91.5	96.8	98.1	66.3	79.0	89.6	92.3
本文方法 w/(F + T + A)	78.2	91.7	96.9	98.1	66.9	79.9	89.7	92.2
本文方法 w/(F + T + S)	78.9	91.2	96.8	98.0	67.5	80.3	89.9	92.4
本文方法 w/(F + T + A + S)	79.1	91.8	97.1	98.2	68.5	80.7	90.1	92.6

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表 5 聚类算法对比

Table 5 Comparison of clustering algorithms

方法	Duke-to-Market					Market-to-Duke
方法	mAP (%)	R-1 (%)	R-5 (%)	R-10 (%)	运行时间 (s)	mAP (%)	R-1 (%)	R-5 (%)	R-10 (%)	运行时间 (s)
Mini-Batch k-means	79.1	91.8	97.1	98.2	811	68.5	80.7	90.1	92.6	908
k-means	79.3	91.8	97.2	98.1	1472	68.8	80.9	90.1	92.6	1669
DBSCAN	80.1	92.3	97.4	98.4	3224	69.9	82.1	90.7	92.9	3643

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