Illumination Unification for Person Re-Identification

• Published in: IEEE transactions on circuits and systems for video technology 2022-10, Vol.32 (10), p.6766-6777
• Main Authors: Zhang, Guoqing, Luo, Zhiyuan, Chen, Yuhao, Zheng, Yuhui, Lin, Weisi
• Format: Article
• Language: English
• Subjects: Cameras; Datasets; Effectiveness; generative adversarial network; Illumination; illumination-adaptive; Image reconstruction; Image restoration; Lighting; Pedestrians; Person re-identification; Retrieval; Source code; Task analysis; Testing; Training
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2022.3169422

The performance of person re-identification (re-ID) is easily affected by illumination variations caused by different shooting times, places and cameras. Existing illumination-adaptive methods usually require annotating cross-camera pedestrians on each illumination scale, which is unaffordable for a long-term person retrieval system. The cross-illumination person retrieval problem presents a great challenge for accurate person matching. In this paper, we propose a novel method to tackle this task, which only needs to annotate pedestrians on one illumination scale. Specifically, (i) we propose a novel Illumination Estimation and Restoring framework (IER) to estimate the illumination scale of testing images taken at different illumination conditions and restore them to the illumination scale of training images, such that the disparities between training images with uniform illumination and testing images with varying illuminations are reduced. IER achieves promising results on illumination-adaptive dataset and proving itself a proper baseline for cross-illumination person re-ID. (ii) we propose a Mixed Training strategy using both Original and Reconstructed images (MTOR) to further improve model performance. We generate reconstructed images that are consistent with the original training images in content but more similar to the restored images in style. The reconstructed images are combined with the original training images for supervised training to further reduce the domain gap between original training images and restored testing images. To verify the effectiveness of our method, some simulated illumination-adaptive datasets are constructed with various illumination conditions. Extensive experimental results on the simulated datasets validate the effectiveness of the proposed method. The source code is available at https://github.com/FadeOrigin/IUReId .