基于图像和特征联合约束的跨模态行人重识别

张玉康; 谭磊; 陈靓影

doi:10.16383/j.aas.c200184

基于图像和特征联合约束的跨模态行人重识别

doi: 10.16383/j.aas.c200184

张玉康^{1, 2,},
谭磊^{1, 2,},
陈靓影^{1, 2,}

1.
华中师范大学教育大数据国家工程实验室武汉 430072
2.
华中师范大学国家数字化学习工程技术研究中心武汉 430072

基金项目: 国家自然科学基金面上项目(61977027), 湖北省科技创新重大专项(2019AAA044)资助

详细信息

作者简介:
张玉康：华中师范大学国家数字化学习工程技术研究中心硕士研究生. 主要研究方向为行人重识别, 生成对抗网络. E-mail: zhangyk@mails.ccnu.edu.cn

谭磊：华中师范大学国家数字化学习工程技术研究中心硕士研究生. 主要研究方向为模式识别和计算机视觉. E-mail: lei.tan@mails.ccnu.edu.cn

陈靓影：华中师范大学国家数字化学习工程技术研究中心教授. 2001 年获得南洋理工计算机科学与工程系博士学位. 主要研究方向为图像处理, 计算机视觉, 模式识别, 多媒体应用. 本文通信作者. E-mail: chenjy@mail.ccnu.edu.cn

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出版历程
- 收稿日期: 2020-04-03
- 录用日期: 2020-10-19
- 网络出版日期: 2021-01-19
- 刊出日期: 2021-08-20

Cross-modality Person Re-identification Based on Joint Constraints of Image and Feature

ZHANG Yu-Kang^{1, 2
,},
TAN Lei^{1, 2
,},
CHEN Jing-Ying^{1, 2
,}

1.
National Engineering Laboratory for Big Data for Education, Central China Normal University, Wuhan 430072
2.
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430072

Funds: Supported by General Program of National Natural Science Foundation of China (61977027), Major scientific and Technological Innovation Projects in Hubei Province (2019AAA044)

More Information

Author Bio:
ZHANG Yu-Kang　Master student at the National Engineering Research Center for E-Learning, Central China Normal University. His research interest covers person re-identification and generative adversarial networks

TAN Lei　Master student at the National Engineering Research Center for E-Learning, Central China Normal University. His research interest covers pattern recognition and computer vision

CHEN Jing-Ying　Professor at the National Engineering Research Center for E-Learning, Central China Normal University. She received her Ph. D. degree from the School of Computer Engineering, Nanyang Technological University, Singapore in 2001. Her research interest covers image processing, computer vision, pattern recognition, and multimedia applications. Corresponding author of this paper

摘要

摘要:
近年来, 基于可见光与近红外的行人重识别研究受到业界人士的广泛关注. 现有方法主要是利用二者之间的相互转换以减小模态间的差异. 但由于可见光图像和近红外图像之间的数据具有独立且分布不同的特点, 导致其相互转换的图像与真实图像之间存在数据差异. 因此, 本文提出了一个基于图像层和特征层联合约束的可见光与近红外相互转换的中间模态, 不仅实现了行人身份的一致性, 而且减少了模态间转换的差异性. 此外, 考虑到跨模态行人重识别数据集的稀缺性, 本文还构建了一个跨模态的行人重识别数据集, 并通过大量的实验证明了文章所提方法的有效性, 本文所提出的方法在经典公共数据集SYSU-MM01上比D2RL算法在 Rank-1和mAP上分别高出4.2 %和3.7 %, 该方法在本文构建的Parking-01数据集的近红外检索可见光模式下比ResNet-50算法在Rank-1和mAP上分别高出10.4 %和10.4 %.
- 跨模态 /
- 行人重识别 /
- 中间模态 /
- 联合约束
Abstract:
In recent years, the research of person re-identification based on visible and near-infrared has attracted widespread attention from the industry. The existing methods mainly use the mutual conversion between them to reduce the difference between their modalities. However, due to the problem of data independence and different distribution between visible image and near-infrared image, there is a large difference between the converted image and the real image, which leads to further improvement of this method. Therefore, this paper proposes a middle modality of conversion between visible and near-infrared modality. So visible and near-infrared can be seamlessly transferred, realizing the identity consistency of person and reducing the difference of conversion between modalities. In addition, considering the scarcity of cross modality person re-identification dataset, this paper also constructs a cross modality person re-identification dataset, and proves the effectiveness of the proposed method through a large number of experiments. In the All-Search Single-shot mode on the SYSU-MM01 dataset, the result of the proposed method is 4.2 % and 3.7 % higher than Rank1 and mAP using the D2RL algorithm, respectively. Compared with ResNet-50 algorithm, the result of the proposed method on the Parking-01 dataset constructed in this paper is 10.4 % and 10.4 % higher in Rank-1 and mAP respectively.
- Cross modality /
- person re-identification /
- middle modality /
- joint constraint

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图 1 本文方法的总体框架

Fig. 1 The overall framework of this method

下载: 全尺寸图片幻灯片

图 2 数据集图像示例

Fig. 2 Example of dataset images

下载: 全尺寸图片幻灯片

图 3 中间模态生成器所生成的中间模态图像

Fig. 3 Middle modality image generated by middle modality generator

下载: 全尺寸图片幻灯片

表 1 SYSU-MM01数据集all-search single-shot模式实验结果

Table 1 Experimental results in all-search single-shot mode on SYSU-MM01 dataset

方法	All-Search Single-shot
方法	R1	R10	R20	mAP
HOG^[19]	2.8	18.3	32.0	4.2
LOMO^[20]	3.6	23.2	37.3	4.5
Two-Stream^[9]	11.7	48.0	65.5	12.9
One-Stream^[9]	12.1	49.7	66.8	13.7
Zero-Padding^[9]	14.8	52.2	71.4	16.0
BCTR^[10]	16.2	54.9	71.5	19.2
BDTR^[10]	17.1	55.5	72.0	19.7
D-HSME^[21]	20.7	62.8	78.0	23.2
MSR^[22]	23.2	51.2	61.7	22.5
ResNet-50*	28.1	64.6	77.4	28.6
cmGAN^[12]	27.0	67.5	80.6	27.8
CMGN^[23]	27.2	68.2	81.8	27.9
D2RL^[13]	28.9	70.6	82.4	29.2
本文方法	33.1	73.9	83.7	32.9

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表 2 SYSU-MM01数据集all-search multi-shot模式实验结果

Table 2 Experimental results in all-search multi-shot mode on SYSU-MM01 dataset

方法	All-Search Multi-shot
方法	R1	R10	R20	mAP
HOG^[19]	3.8	22.8	37.7	2.16
LOMO^[20]	4.70	28.3	43.1	2.28
Two-Stream^[9]	16.4	58.4	74.5	8.03
One-Stream^[9]	16.3	58.2	75.1	8.59
Zero-Padding^[9]	19.2	61.4	78.5	10.9
ResNet-50*	30.0	66.2	75.7	24.6
cmGAN^[12]	31.5	72.7	85.0	22.3
本文方法	33.4	70.0	78.7	27.0

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表 3 SYSU-MM01数据集indoor-search single-shot模式实验结果

Table 3 Experimental results in indoor-search single-shot mode on SYSU-MM01 dataset

方法	indoor-search single-shot
方法	R1	R10	R20	mAP
HOG^[19]	3.2	24.7	44.6	7.25
LOMO^[20]	5.8	34.4	54.9	10.2
Two-Stream^[9]	15.6	61.2	81.1	21.2
One-Stream^[9]	17.0	63.6	82.1	23.0
Zero-Padding^[9]	20.6	68.4	85.8	27.0
CMGN^[23]	30.4	74.2	87.5	40.6
ResNet-50*	31.0	78.2	90.3	41.9
cmGAN^[12]	31.7	77.2	89.2	42.2
本文方法	31.1	79.5	89.1	41.3

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表 4 SYSU-MM01数据集indoor-search multi-shot模式实验结果

Table 4 Experimental results in indoor-search multi-shot mode on SYSU-MM01 dataset

方法	indoor-search multi-shot
方法	R1	R10	R20	mAP
HOG^[19]	4.8	29.1	49.4	3.51
LOMO^[20]	7.4	40.4	60.4	5.64
Two-Stream^[9]	22.5	72.3	88.7	14.0
One-Stream^[9]	22.7	71.8	87.9	15.1
Zero-Padding^[9]	24.5	75.9	91.4	18.7
ResNet-50*	29.9	66.2	75.7	24.5
cmGAN^[12]	37.0	80.9	92.3	32.8
本文方法	37.2	76.0	83.8	33.8

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表 5 近红外检索可见光模式的实验结果

Table 5 Experimental results of near infrared retrieval visible mode

方法	近红外−>可见光
方法	R1	R10	R20	mAP
ResNet-50*	15.5	39.7	51.9	19.3
本文方法	25.9	53.8	62.8	29.7

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表 6 可见光检索近红外模式的实验结果

Table 6 Experimental results of visible retrieval near infrared mode

方法	可见光−>近红外
方法	R1	R10	R20	mAP
ResNet-50*	20.2	45.6	50.0	14.7
本文方法	31.6	48.2	56.1	19.7

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表 7 不同模态转换的实验结果

Table 7 Experimental results of different mode conversion

方法	R1	R10	R20	mAP
ResNet-50*	28.1	64.6	77.4	28.6
转为可见光	29.6	69.8	80.5	30.7
转为近红外	30.8	71.5	83.2	31.2
本文提出的方法	33.1	73.9	83.7	32.9

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表 8 有无循环一致性损失的实验结果

Table 8 Experimental results with or without loss of cycle consistency

方法	R1	R10	R20	mAP
无循环一致性	29.6	67.1	78.3	31.1
有循环一致性	33.1	73.9	83.7	32.9

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