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

• Published in: International journal of computer vision Vol. 128; no. 6; pp. 1765 - 1785
• Main Authors: Wu, Ancong, Zheng, Wei-Shi, Gong, Shaogang, Lai, Jianhuang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2020; Springer; Springer Nature B.V
• Subjects: Artificial Intelligence; Color imagery; Computer Imaging; Computer Science; Datasets; Feature extraction; Image Processing and Computer Vision; Indoor environments; Infrared imaging; Learning; Model matching; Pattern Recognition; Pattern Recognition and Graphics; Preservation; Similarity; Surveillance; Surveillance systems; Vision; RGB-infrared matching; Person re-identification; Cross-modality model
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-019-01290-1

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 .