Day-Night Cross-domain Vehicle Re-identification
Authors: Hongchao Li, Jingong Chen, Aihua Zheng, Yong Wu, Yonglong Luo

Previous advances in vehicle re-identification (ReID) are mostly reported under favorable lighting conditions while cross-day-and-night performance is neglected which greatly hinders the development of related traffic intelligence applications. This work instead develops a novel Day-Night Dual-domain Modulation (DNDM) vehicle re-identification framework for day-night cross-domain traffic scenarios. Specifically a unique night-domain glare suppression module is provided to attenuate the headlight glare from raw nighttime vehicle images. To enhance vehicle features under low-light environments we propose a dual-domain structure enhancement module in the feature extractor which enhances geometric structures between appearance features. To alleviate day-night domain discrepancies we develop a cross-domain class awareness module that facilitates the interaction between appearance and structure features in both domains. In this work we address the Day-Night cross-domain ReID (DN-ReID) problem and provide a new cross-domain dataset named DN-Wild including day and night images of 2286 identities giving in total 85945 daytime images and 54952 nighttime images. Furthermore we also take into account the matter of balance between day and night samples and provide a dataset called DN-348. Exhaustive experiments demonstrate the robustness of the proposed framework in the DN-ReID problem. The code and benchmark are released at https://github.com/chenjingong/DN-ReID.