Disentangled reflectance-ambient feature learning for day-night vehicle re-identification

Abstract

Vehicle re-identification across different time domains is a critical task in intelligent surveillance systems, aiming to match the same vehicle across multiple non-overlapping cameras under varying lighting conditions. Existing methods often struggle to handle the domain discrepancy between daytime and nighttime images, mainly due to lighting variation and glare. To address this, a novel framework named Reflectance-Ambient Feature Learning (RAFL) is proposed, which disentangles structural reflectance features from ambient lighting effects using offline reflectance decomposition. By integrating separated batch normalization and a domain alleviation module, the framework effectively minimizes the domain gap while preserving identity-discriminative features. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance, with up to 4.0 % improvement in Rank-1 accuracy and over 1.5 % gain in mean Average Precision compared to existing methods. This highlights the effectiveness of feature disentanglement for robust cross-domain vehicle re-identification in real-world surveillance.