originally serve object detection or target tracking task in which camera's height is too high to get a suitable spatial resolution for ReID task (shown in Figure 1b). As far as we are concerned, the only UAV platform dataset especially for the vehicle ReID task is VRAI [4] and it's difficult to comprehensively examine the effectiveness of the algorithms using one single dataset for research. The scarcity of the dataset restricts the research on vehicle ReID on UAV platforms.  
* **Viewpoint**: the image features of the UAV platform are very different from that of the fixed surveillance cameras. Fixed surveillance cameras are installed in the well-chosen location along the streets so that the discriminative visual features behind the windshield can be easily found, and there is little occlusion in the pictures. The UAV moves relatively flexible, having a wide range of height changes (3\(\sim\)100 m) and angle changes (pitch angle \(-90^{\circ}\)\(\sim\)\(-45^{\circ}\), yaw angle \(-90^{\circ}\)\(\sim\)\(90^{\circ}\)) (Figure 2a,b), and less detailed visual features at large angle of inclination (Figure 2c). What's more, the image spatial resolution is relatively reduced and there is unpredictable occlusion during the flying (Figure 2d).
* **Confusion**: the image features of the UAV platform exacerbated the problem of large intra-class difference and small inter-class difference. There are three major challenges  
Figure 1: Different datasets for different tasks and captured by different platform.  
in Vehicle ReID task. **Firstly**, the number of classes is very large. Researchers often use classification loss when training the network in the vehicle ReID task, which means every unique vehicle (every ID) is treated as an independent class. For example, there are 13,021 classes in VRAI dataset, while there are only 80 classes in Microsoft Common Objects in Context dataset (COCO) [5], which is a famous target detection and segmentation dataset. **Secondly**, the intra-class difference is large. The same car under the different cameras at variations in viewpoints, altitudes, and under different illuminations may seem completely different. Figure 2(a) shows two examples. **Thirdly**, the inter-class difference is tiny. Different vehicles with same brand or same paint may seem of no difference, which can only be distinguished by discriminative visual details. Figure 2(b) shows four different vehicles with same paint. The problem of viewpoint aggravates the confusion of inter-class and intra-class and the confusion is relative to the mixture of features among different classes in feature space.  
Typically, a standard ReID system contains two main components: feature extraction and metric learning [6; 7; 8]. The former focus on extracting clear features from images and the latter concentrates on optimizing the feature distance in feature space. Recently most of the researchers are attracted to the proposal of novel feature extraction networks, leaving the metric learning with barely research. However, the problems of UAV vehicle ReID mentioned above are just relative to the phenomenon of mixed feature space and the weakness of the metric learning ability. Thus, we introduce joint normalized virtual softmax loss (NV-softmax loss) and triplet loss to guide a better feature distance learning. In the practical application of vehicle ReID technology, comparing with the other softmax-base method, ours can return more accurate query results, thus effectively identifying criminal vehicles.  
In summary, vehicle ReID task on UAV platform involves many new challenges and difficulties, which requires detailed discrimination ability. In this paper, we build a new UAV dataset called VRAI-AIR to provide effective data support for complex backgrounds and various viewpoints of UAV images. Furthermore, we design normalized virtual softmax loss with triplet loss to solve viewpoint and confusion problem. We will elaborate in the latter two sections of Dataset and Method.