Temporal Weighting Appearance-Aligned Network for Nighttime Video Retrieval

Video-based person re-identification (ReID) aims at re-identifying video sequences of a specified person from videos captured by disjoint cameras. Existing datasets and works on this task all focus on daytime scenarios and cannot adapt well to the nighttime scenarios, which is also of significant importance for practical applications. In this letter, we contribute a new dataset for nighttime video-based ReID, termed NIVIR, which contains 800 identities with over 228,000 images. NIVIR contains video shots under various lighting conditions, different weathers, and complex scenarios, which is consistent with the real nighttime outdoor surveillance. Furthermore, we propose a temporal weighting appearance-aligned network (TWAN) for nighttime video-based ReID, which is composed of a correlation-based appearance-aligned module (CAM) and a temporal weighting module (TWM). Specifically, CAM is proposed to reconstruct the adjacent feature maps to guarantee the appearance alignment between the central frame and its adjacent frames. TWM is designed to evaluate the frame quality of a tracklet and generate temporal weights to enhance the video representation. Extensive experiments conducted on our new NIVIR dataset demonstrate that the proposed TWAN outperforms the state-of-the-art methods. We believe that our NIVIR dataset and the comprehensive attempts for solving the nighttime ReID problem will push forward the development of the ReID research community.