Night Person Re-Identification and a Benchmark

Abstract

Person re-identification is an important problem in computer vision fields due to its widely application. However, most of existing person re-identification methods are evaluated in daytime scenarios which is still far from real applications. In this paper, we pay attention to the night scenario person re-identification problem which most of works are not focused on. For this purpose, we contribute a large and real-scenario person re-identification dataset for night scenario named KnightReid, which aims to bridge the gap between theoretical research and practical application. To the best of our knowledge, the KnightReid dataset is the first night scenario dataset for the person re-identification which distinguishes existing works. Furthermore, by carefully examining the properties of night scenario data, we propose to combine image denoising networks with common used person re-identification networks to adapt to this kind of problem. Besides, we provide a comprehensive benchmark result that is evaluated on the dataset. The extensive experiments convince the effectiveness of the proposed model.