Vehicle Re-Identification in Aerial Imagery: Dataset and Approach

Peng Wang, Bingliang Jiao, Lu Yang, Yifei Yang, Shizhou Zhang, Wei Wei, Yanning Zhang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 460-469


In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs at different locations, with diverse view-angles and flight-altitudes. We manually label a variety of vehicle attributes, including vehicle type, color, skylight, bumper, spare tire and luggage rack. Furthermore, for each vehicle image, the annotator is also required to mark the discriminative parts that helps them to distinguish this particular vehicle from others. Besides the dataset, we also design a specific vehicle ReID algorithm to make full use of the rich annotation information. It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforming the evaluated baselines and state-of-the-art vehicle ReID approaches.

Related Material

author = {Wang, Peng and Jiao, Bingliang and Yang, Lu and Yang, Yifei and Zhang, Shizhou and Wei, Wei and Zhang, Yanning},
title = {Vehicle Re-Identification in Aerial Imagery: Dataset and Approach},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}