The ReID problem [1,22] is first explored and applied to humans. Compared with pedestrian ReID, vehicle ReID is more challenging. Firstly, vehicles tend to have high similarity in appearance, especially in the case of the same brand, model, or color. A higher similarity makes vehicle re-identification more challenging because relatively few features may distinguish different vehicles, and there is little difference between features. Second, vehicle re-identification may face more significant pose variation than human re-identification. Vehicles may appear at different angles, positions, and rotations, resulting in changes in the geometry and appearance characteristics of the vehicle, which increases the difficulty of matching and alignment. Traditionally, vehicle Re-ID problems have been solved by combining sensor data with other clues [23,24,25,26,27,28], such as vehicle travel time [23] and wireless magnetic sensors [24]. Although the sensor technology can obtain better detection results, it cannot meet the needs of practical applications because of its high detection cost. In theory, based on the vehicle license plate number, feature recognition technology is the most reliable and most accurate again [25,26]. However, the camera’s multi-angle, illumination, and resolution significantly influence license plate identification accuracy. Additionally, criminals block, decorate, forge, or remove license plates, making re-identifying vehicles only by license plate information less reliable. Accordingly, researchers have considered vehicle attributes and appearance characteristics, such as shape, color, and texture [27,28].

With the development of neural networks, deep learning-based approaches have outshone others [5,29]. Significant changes in camera angles can lead to substantial differences in local critical areas for vehicle re-identification, which leads to low precision. The hybrid pyramidal graph network (HPGN) [30] proposes a novel pyramid graph network, targeting features closely connected behind the backbone network to explore multi-scale spatial structural features. Zheng et al. [5] proposed the deep feature representations jointly guided by the meaningful attributes, including camera views, vehicle types and colors (DF-CVTC), a unified depth convolution framework for the joint learning of depth feature representations guided by meaningful attributes, including camera view, vehicle type, and color of vehicle re-identification. Huang et al. [29] raised multi-granularity deep feature fusion with multiple granularity (DFFMG) methods or vehicle re-identification, which uses global and local feature fusion to segment vehicle images along two directions (i.e., vertical and horizontal), and integrates discriminant information of different granularity. Graph interactive transformer (GiT) [31] proposes a structure where charts and transformers constantly interact, enabling close collaboration between global and local features for vehicle re-identification. The efficient multiresolution network (EMRN) [32] proposes a multiresolution feature dimension uniform module to fix dimensional features from images of varying resolutions.

Although the current vehicle ReID method plays a specific role in the fixed camera perspective, the vehicle space photographed from the UAV perspective changes significantly, and extracting features from the top–down vertical angle is difficult. Moreover, the shooting angle of UAVs is complex. UAVs can overlook or squint at a target or scene at different angles, resulting in viewpoint changes in the image. The current method needs to be revised to solve the above problems well, and further research is required.