Color Space Learning for Cross-Color Person Re-Identification

TL;DR

This paper introduces Color Space Learning (CSL), a novel approach for cross-color person re-identification that reduces color sensitivity through augmentation and pixel-level transformation, and presents a new RGB-Infrared benchmark.

Contribution

The paper proposes CSL with two modules to improve cross-color Person ReID and introduces the NTU-Corridor benchmark for RGB-Infrared re-identification.

Findings

CSL outperforms state-of-the-art methods on multiple benchmarks.

The approach effectively reduces color sensitivity in re-identification tasks.

The NTU-Corridor dataset is the first with comprehensive privacy agreements.

Abstract

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