Color Space Learning for Cross-Color Person Re-Identification
<|reference_start|>Color Space Learning for Cross-Color Person Re-Identification: The primary color profile of the same identity is assumed to remain consistent in typical Person Re-identification (Person ReID) tasks. However, this assumption may be invalid in real-world situations and images hold variant color profiles, because of cross-modality cameras or identity with different clothing. To address this issue, we propose Color Space Learning (CSL) for those Cross-Color Person ReID problems. Specifically, CSL guides the model to be less color-sensitive with two modules: Image-level Color-Augmentation and Pixel-level Color-Transformation. The first module increases the color diversity of the inputs and guides the model to focus more on the non-color information. The second module projects every pixel of input images onto a new color space. In addition, we introduce a new Person ReID benchmark across RGB and Infrared modalities, NTU-Corridor, which is the first with privacy agreements from all participants. To evaluate the effectiveness and robustness of our proposed CSL, we evaluate it on several Cross-Color Person ReID benchmarks. Our method surpasses the state-of-the-art methods consistently. The code and benchmark are available at: https://github.com/niejiahao1998/CSL<|reference_end|>
@article{nie2024color,
    title={Color Space Learning for Cross-Color Person Re-Identification},
    author={Jiahao Nie, Shan Lin, Alex C. Kot},
    journal={arXiv preprint arXiv:2405.09487},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09487},
    primaryClass={cs.CV}
}