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Learning Deep RGBT Representations for Robust Person Re-identification

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Abstract

Person re-identification (Re-ID) is the scientific task of finding specific person images of a person in a non-overlapping camera networks, and has achieved many breakthroughs recently. However, it remains very challenging in adverse environmental conditions, especially in dark areas or at nighttime due to the imaging limitations of a single visible light source. To handle this problem, we propose a novel deep red green blue (RGB)-thermal (RGBT) representation learning framework for a single modality RGB person Re-ID. Due to the lack of thermal data in prevalent RGB Re-ID datasets, we propose to use the generative adversarial network to translate labeled RGB images of person to thermal infrared ones, trained on existing RGBT datasets. The labeled RGB images and the synthetic thermal images make up a labeled RGBT training set, and we propose a cross-modal attention network to learn effective RGBT representations for person Re-ID in day and night by leveraging the complementary advantages of RGB and thermal modalities. Extensive experiments on Market 1501, CUHK03 and DukeMTMC-reID datasets demonstrate the effectiveness of our method, which achieves state-of-the-art performance on all above person Re-ID datasets.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61976002, 61976003 and 61860206004), Natural Science Foundation of Anhui Higher Education Institutions of China (No. KJ2019A0033), and the Open Project Program of the National Laboratory of Pattern Recognition (No. 201900046).

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Authors and Affiliations

  1. Anhui Provincial Key Laboratory of Multi-modal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Ai-Hua Zheng, Zi-Han Chen, Cheng-Long Li, Jin Tang & Bin Luo

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  1. Ai-Hua Zheng
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  2. Zi-Han Chen
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  3. Cheng-Long Li
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  4. Jin Tang
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Correspondence to Cheng-Long Li.

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Recommended by Associate Editor Jangmyung Lee

Ai-Hua Zheng received the B. Eng. and Ph. D. degrees in computer science and technology from Anhui University, China in 2006 and 2008, respectively. And she received the Ph. D. degree in computer science from University of Greenwich, UK in 2012. She visited University of Stirling and Texas State University from June to September in 2013 and from September 2019 to August 2020, respectively. She is currently an associate professor and Ph. D. supervisor in School of Computer Science and Technology in Anhui University, China. As the first author or corresponding author, she has published more than 40 academic papers, including top conferences papers in American Association for Artificial Intelligence Conference on Artificial Intelligence (AAAI) and the International Joint Conference on Artificial Intelligence (IJCAI), and authoritative journals in IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMCS), Pattern Recognition (PR), Pattern Recognition Letters (PRL), Neurocomputing (NeuCom), Cognitive Computation (CogCom), the IEEE International Symposium on Network Computing and Applications (NCA), etc. She is a member of China Computer Federation (CCF) and China Society of Image and Graphics (CSIG). She is also serving as reviewers for representative conferences and journals, including AAAI, IJCAI, IEEE Transactions on Image Processing (TIP), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Intelligent Transportation Systems (TITS), PR, etc. She has obtained the Best Paper Award in the International Conference on Software Engineering Research, Management and Applications (SERA) 2017 and the Best Student Paper Award in the workshop in the IEEE International Conference on Multimedia and Expo (ICME) 2019.

Her research interests include vision based artificial intelligence and pattern recognition, especially on person/vehicle reidentification, audio visual computing, and multi-modal intelligence.

Zi-Han Chen received the B. Eng. degree in software engineering from Anhui University, China in 2018. He is currently a master student in computer science and technology from Anhui University, China.

His research interests include computer vision, person re-identification and machine learning.

Cheng-Long Li received the M. Sc. and Ph. D. degrees in computer science from School of Computer Science and Technology, Anhui University, China in 2013 and 2016, respectively. From 2014 to 2015, he worked as a visiting student with School of Data and Computer Science, Sun Yat-sen University, China. He was a postdoctoral research fellow at the Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), China. He is currently an associate professor at School of Computer Science and Technology, Anhui University, China. He was a recipient of the ACM Hefei Doctoral Dissertation Award in 2016.

His research interests include computer vision and deep learning.

Jin Tang received the B. Eng. degree in automation and the Ph. D. degree in computer science from Anhui University, China in 1999 and 2007, respectively. He is a professor with School of Computer Science and Technology, Anhui University.

His research interests include computer vision, pattern recognition and machine learning.

Bin Luo received the B. Eng. degree in electronics, and the M. Eng. degree in computer science from Anhui University, China in 1984 and 1991, respectively, and the Ph. D. degree in computer science from University of York, UK in 2002. From 2000 to 2004, he was a research associate with University of York, UK. He is currently a professor with Anhui University, China.

His research interests include graph spectral analysis, large image database retrieval, image and graph matching, statistical pattern recognition, digital watermarking and information security.

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Zheng, AH., Chen, ZH., Li, CL. et al. Learning Deep RGBT Representations for Robust Person Re-identification. Int. J. Autom. Comput. 18, 443–456 (2021). https://doi.org/10.1007/s11633-020-1262-z

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