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International Journal of Computer Vision
Published in: International Journal of Computer Vision 6/2020

03-02-2020

RGB-IR Person Re-identification by Cross-Modality Similarity Preservation

Authors: Ancong Wu, Wei-Shi Zheng, Shaogang Gong, Jianhuang Lai

Published in: International Journal of Computer Vision | Issue 6/2020

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Abstract

Person re-identification (Re-ID) is an important problem in video surveillance for matching pedestrian images across non-overlapping camera views. Currently, most works focus on RGB-based Re-ID. However, RGB images are not well suited to a dark environment; consequently, infrared (IR) imaging becomes necessary for indoor scenes with low lighting and 24-h outdoor scene surveillance systems. In such scenarios, matching needs to be performed between RGB images and IR images, which exhibit different visual characteristics; this cross-modality matching problem is more challenging than RGB-based Re-ID due to the lack of visible colour information in IR images. To address this challenge, we study the RGB-IR cross-modality Re-ID (RGB-IR Re-ID) problem. Rather than applying existing cross-modality matching models that operate under the assumption of identical data distributions between training and testing sets to handle the discrepancy between RGB and IR modalities for Re-ID, we cast learning shared knowledge for cross-modality matching as the problem of cross-modality similarity preservation. We exploit same-modality similarity as the constraint to guide the learning of cross-modality similarity along with the alleviation of modality-specific information, and finally propose a Focal Modality-Aware Similarity-Preserving Loss. To further assist the feature extractor in extracting shared knowledge, we design a modality-gated node as a universal representation of both modality-specific and shared structures for constructing a structure-learnable feature extractor called Modality-Gated Extractor. For validation, we construct a new multi-modality Re-ID dataset, called SYSU-MM01, to enable wider study of this problem. Extensive experiments on this SYSU-MM01 dataset show the effectiveness of our method. Download link of dataset: https://​github.​com/​wuancong/​SYSU-MM01.
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Appendix
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Metadata
Title
RGB-IR Person Re-identification by Cross-Modality Similarity Preservation
Authors
Ancong Wu
Wei-Shi Zheng
Shaogang Gong
Jianhuang Lai
Publication date
03-02-2020
Publisher
Springer US
Published in
International Journal of Computer Vision / Issue 6/2020
Print ISSN: 0920-5691
Electronic ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-019-01290-1

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