联合DD-GAN和全局特征的井下人员重识别方法

doi:10.19665/j.issn1001-2400.2021.05.023

Abstract

Abstract:

It is of great significance to the smarter safe production of coal to control and analyze the video data obtained by multiple surveillance cameras in each important area of the coal mine,and to locate and identify the workers in the video.However,due to the dim light and uneven illumination in the mine,the existing conventional method of person re-identification (Re-ID) cannot meet the requirements in the coal mine.In order to solve the above problems,this paper proposes a Re-ID method combining the Dual-Discriminator Generative Adversarial Network and global feature in coal mine.First,the Dual-Discriminator Generative Adversarial Network(DD-GAN) is designed to enhance and restore images with dim light or uneven illumination,providing a more discriminating image foundation for person re-identification.Second,the Global Feature Network for Re-ID is proposed in the coal mine to solve the miner’s identification problem,and the methods of Random erasing and Re-ranking of k-reciprocal nearest neighbors are used to improve further the robustness and accuracy of the Re-ID network.Finally,the Miner-CUMT dataset suitable for special downhole scenes is constructed,which solves the problems of the single scene of existing sample sets and improves the generalization of the method presented in this paper.The proposed method has achieved good results in the Miner-CUMT dataset and actual scene in the coal mine,which lays an important foundation for the development of intelligent and safe production in coal mines.

Key words: smart coal mines, person re-identification, convolutional neural network, image enhancement, generative adversarial networks

CLC Number:

TP391

SUN Yanjing,WEI Li,ZHANG Nianlong,YUN Xiao,DONG Kaiwen,GE Min,CHENG Xiaozhou,HOU Xiaofeng. Person re-identification method combining the DD-GAN and Global feature in a coal mine[J].Journal of Xidian University, 2021, 48(5): 201-211.

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References 24

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方法	DD-GAN	mAP	Rank-1	Rank-5
GFN	×	58.7	87.2	94.4
GFN	√	64.2	89.7	95.4

方法	mAP	Rank-1	Rank-5	Rank-10
GFN+None	58.7	87.2	94.4	96.3
GFN+RE	62.9	89.8	96.3	97.5
GFN+Re-ranking	69.1	90.0	95.4	96.5
GFN+RE+Re-ranking(文中方法)	78.3	92.5	95.7	96.7

Person re-identification method combining the DD-GAN and Global feature in a coal mine

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