1. Grayscale Enhancement Colorization Network for Visible-Infrared Person Re-Identification.
- Author
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Zhong, Xian, Lu, Tianyou, Huang, Wenxin, Ye, Mang, Jia, Xuemei, and Lin, Chia-Wen
- Subjects
GENERATIVE adversarial networks ,ION channels ,INFRARED imaging ,IMAGE registration ,IMAGE color analysis ,GALLIUM nitride - Abstract
Visible-infrared person re-identification (VI-ReID) is an emerging and challenging cross-modality image matching problem because of the explosive surveillance data in night-time surveillance applications. To handle the large modality gap, various generative adversarial network models have been developed to eliminate the cross-modality variations based on a cross-modal image generation framework. However, the lack of point-wise cross-modality ground-truths makes it extremely challenging to learn such a cross-modal image generator. To address these problems, we learn the correspondence between single-channel infrared images and three-channel visible images by generating intermediate grayscale images as auxiliary information to colorize the single-modality infrared images. We propose a grayscale enhancement colorization network (GECNet) to bridge the modality gap by retaining the structure of the colored image which contains rich information. To simulate the infrared-to-visible transformation, the point-wise transformed grayscale images greatly enhance the colorization process. Our experiments conducted on two visible-infrared cross-modality person re-identification datasets demonstrate the superiority of the proposed method over the state-of-the-arts. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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