Vehicle Re-Identification in Aerial Images and Videos: Dataset and Approach.

In this work, we propose a large-scale dataset, VRAI, and an effective Orientation Adaptive and Salience Attentive (OASA) Network for vehicle re-identification (ReID) in aerial imagery. The VRAI dataset includes two subsets: VRAI-Image, which contains over 137,000 images of 13,000 vehicle instances, and VRAI-Video, which comprises more than 14,000 video trajectories of 7,000 identities. To our best knowledge, this is the largest dataset for UAV-based vehicle ReID, and the first dataset proposed for video-based ReID under UAV views. Based on the VRAI dataset, we design an OASA network to address two crucial challenges of vehicle ReID in aerial imagery. Firstly, the significant vehicle orientation variations in aerial images could cause great vehicle pattern deformations, making it difficult to identify vehicles across UAV views. To overcome this challenge, in our OASA, an orientation adaptive dynamic convolution module is designed, which constructs customized kernels for each vehicle instance to extract orientation-invariant features. Besides, the unique vertical view and long focal length of the UAV platform often render many salient vehicle attributes, such as logos and license plates, invisible, which brings a great challenge to ReID models to extract distinguishable vehicle features. To address this issue, in the OASA, we design a transformer-based salience attentive module (Trans-Attn) that guides the model to focus on subtle yet discriminative clues of vehicle instances in aerial imagery. Through extensive experiments, both of our designed modules are verified effective. Besides, our OASA model outperforms state-of-the-art algorithms both on our VRAI dataset and other surveillance-based datasets. Our VRAI dataset is available in https://github.com/JiaoBL1234/VRAI-Dataset.