Accurate road user localization in aerial images captured by unmanned aerial vehicles

Abstract

Unmanned aerial vehicles (UAVs) have become increasingly popular for traffic data collection. However, the depth relief of road users and the perspective distortion of the onboard camera induce nonnegligible measurement errors while exploiting UAVs to localize road users. To address this issue, this paper presents a method for accurate road user location estimation in aerial images. First, a deep-learning-based method was employed to detect road users in aerial images using oriented bounding boxes. Then, the localization error induced by the depth relief and perspective distortion was examined and modeled, based on which an error compensation scheme was developed to offset the localization error for each road user so that higher localization accuracy is attainable. Field experiments were conducted to evaluate the proposed method's performance. The results demonstrated a promising accuracy in estimating the location of road users, signifying the method's potential to improve the credibility of UAVs in traffic applications.