RainReID: Person Re‐Identification in Rainy Weather and a Large‐Scale Dataset

Person re‐identification (Re‐ID) is a fundamental problem in computer vision that focuses on matching individuals across nonoverlapping camera views. It plays a crucial role in large‐scale intelligent surveillance systems, enabling efficient image retrieval of specific persons of interest. Although state‐of‐the‐art Re‐ID methods have achieved remarkable progress in diverse scenarios, complex weather scenarios (e.g., rain) remain an underexplored factor. Rainy day is one of the most common types of weather, and Re‐ID in rainy weather scenario is confronted with complex challenges, such as occlusions caused by umbrellas or raindrops, illumination variations during indoor–outdoor transitions and so on. To develop the Re‐ID in complex weather scenarios, this work introduce the first person re‐identification work under real rainy weather. A large‐scale dataset, RainReID, is established after a long and arduous effort due to the difficulty of data acquisition. RainReID contains 596 identities and 27,617 images captured from 7 cameras, and involves both outdoor and indoor conditions. A dual‐branch scene‐adaptive (DSA) framework is proposed as a deep learning based benchmark, which is used to enhance the robustness especially for indoor–outdoor Re‐ID. Extensive experiments demonstrate the challenge of RainReID, and the effectiveness of DAS is proved under rainy weather. RainReID will be released at https://github.com/Qingming‐Leng/RainReID .