基于GAN的无监督域自适应行人重识别

doi:10.11959/j.issn.1000-0801.2021016

Abstract

Abstract:

Aiming at the problem that the inaccurate clustering in the unsupervised domain adaptive pedestrian re-recognition results in low network recognition accuracy, an unsupervised domain adaptive pedestrian re-recognition method based on generative confrontation network was proposed.Firstly, the CNN model was optimized by using the batch normalization layer after the pooling layer, deleting a fully connected layer and adopting the Adam optimizer.Secondly, the cluster error was analyzed and the important parameter in the cluster was decided based on minimum error rate Bayesian decision theory.Finally, the generative adversarial network was utilized to adjust the cluster.These steps effectively improved the recognition accuracy of unsupervised domain adaptive person re-identification.In the case of the source domain Market-1501 and the target domain DukeMTMC-reID, experimental results show that mAP and Rank-1 can reach 53.7% and 71.6%, respectively.

Key words: unsupervised domain adaptive, person re-identification, generative adversarial network

CLC Number:

TP391

Shengsheng ZHENG, Haibing YIN, Xiaofeng HUANG, Tianjie ZHANG. GAN-based unsupervised domain adaptive person re-identification[J]. Telecommunications Science, 2021, 37(2): 99-106.

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References 18

[1]	ZHENG L , YANG Y , HAUPTMANN A G ,et al. Person re-identification:past,present and future[J]. arXiv:1610.02984, 2012.
[2]	王志宏, 杨震 . 人工智能技术研究及未来智能化信息服务体系的思考[J]. 电信科学, 2017,33(5): 1-11.
	WANG Z H , YANG Z . Research on artificial intelligence technology and the future intelligent information service architecture[J]. Telecommunications Science, 2017,33(5): 1-11.
[3]	杨锋, 许玉, 尹梦晓 ,等. 基于深度学习的行人重识别综述[J]. 计算机应用, 2020,40(5): 1243-1252.
	YANG F , XU Y , YIN M X ,et al. Review on deep learning-based pedestrian re-identification[J]. Journal of Computer Applications, 2020,40(5): 1243-1252.
[4]	罗浩 . 深度学习时代的行人重识别技术[J]. 人工智能, 2019(2): 40-49.
	LUO H . Person re-identification technology in the era of deep learning[J]. Artificial Intelligence, 2019(2): 40-49.
[5]	GOODFELLOW I , POUGETABADIE J , MIRZA M ,et al. Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2. New York:ACM Press, 2014: 2672-2680.
[6]	贾川民, 赵政辉, 王苫社 . 基于神经网络的图像视频编码[J]. 电信科学, 2019,35(5): 32-42.
	JIA C M , ZHAO Z H , WANG S S . Neural network based image and video coding technologies[J]. Telecommunications Science, 2019,35(5): 32-42.
[7]	王万良, 李卓蓉 . 生成式对抗网络研究进展[J]. 通信学报, 2018,39(2): 135-148.
	WANG W L , LI Z R . Advances in generative adversarial network[J]. Journal on Communications, 2018,39(2): 135-148.
[8]	陈亮, 吴攀, 刘韵婷 ,等. 生成对抗网络GAN的发展与最新应用[J]. 电子测量与仪器学报, 2020,34(6): 70-78.
	CHEN L , WU P , LIU Y T ,et al. Development and application of the latest generation against the network of GAN[J]. Journal of Electronic Measurement and Instrumentation, 2020,34(6): 70-78.
[9]	PAN S J , TSANG I W , KWOK J T ,et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011,22(2): 199-210.
[10]	CHEN B , LAM W , TSANG I W ,et al. Extracting discriminative concepts for domain adaptation in text mining[C]// Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2009: 179-188.
[11]	NIGAM K , MCCALLUM A , MITCHELL T . Semi-supervised text classification using EM[M]// Semi-supervised learning. Cambridge: MIT Press, 2006: 33-38.
[12]	DENG W , ZHENG L , YE Q ,et al. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[J]. arXiv:1711.07027, 2018.
[13]	WEI L , ZHANG S , GAO W ,et al. Person transfer GAN to bridge domain gap for person re-identification[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 79-88.
[14]	WANG J , ZHU X , GONG S ,et al. Transferable joint attribute-identity deep learning for unsupervised person re-identification[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 2275-2284.
[15]	LI Y , YANG F , LIU Y ,et al. Adaptation and re-identification network:an unsupervised deep transfer learning approach to person re-identification[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 172-178.
[16]	SONG L , WANG C , ZHANG L ,et al. Unsupervised domain adaptive re-identification:theory and practice[J]. arXiv:1807.11334, 2018.
[17]	MA L , JIA X , SUN Q ,et al. Pose guided person image generation[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. New York:ACM Press, 2017: 406-416.
[18]	XIONG F , XIAO Y , CAO Z ,et al. Towards good practices on building effective CNN baseline model for person re-identification[J]. arXiv:1807.11042, 2018.

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数据集	训练集		测试集
数据集	ID	图像	ID	图像
Market-1501	751	12 936	750	19 732
DukeMTMC-reID	702	16 522	702	19 889

P	mAP	Rank-1	Rank-5	Rank-10
1.0×10^-3	50.3%	69.6%	80.1%	83.8%
1.1×10^-3	50.5%	69.7%	80.5%	84.1%
1.2×10^-3	51.0%	69.8%	81.1%	84.6%
1.3×10^-3	51.5%	69.8%	81.2%	84.9%
1.4×10^-3	51.9%	70.0%	81.2%	84.8%
1.5×10^-3	52.2%	70.2%	81.8%	85.5%
1.6×10^-3	52.0%	70.1%	81.6%	85.2%
1.7×10^-3	51.6%	69.9%	81.3%	85.3%
1.8×10^-3	50.7%	69.7%	80.9%	84.3%
1.9×10^-3	50.2%	69.6%	79.8%	83.7%
2.0×10^-3	49.8%	68.6%	80.3%	83.6%

方法	mAP	Rank-1	Rank-5	Rank-10
Direct Transfer	11.9%	27.3%	41.2%	47.1%
Self-training Baseline	24.7%	40.8%	53.9%	60.5%
SPGAN^[12]	26.2%	46.4%	62.3%	68.0%
TJ-AIDL^[14]	23.0%	44.3%	59.6%	65.0%
ARN^[15]	33.4%	60.2%	73.9%	79.5%
UDA^[16]	49.0%	68.4%	80.1%	83.5%
本文算法	53.7%	71.6%	83.3%	88.2%

GAN-based unsupervised domain adaptive person re-identification

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