An Investigation on Accurate Road User Location Estimation in Aerial Images Collected by Drones

Unmanned aerial vehicles (UAVs) have recently become popular in collecting positional data of road users. In comparison to other tools at ground level, UAVs have the advantages of low cost, wider view coverage, and significantly less occlusion. However, the depth relief of road users and the perspective distortion of the onboard camera induce nonnegligible error while applying UAVs for localization of road users. This study proposed a method for accurate road user localization in aerial images. First, the localization error induced by the depth relief and perspective distortion was examined and modeled. Then, a deep-learning-based method was applied for automatic road user detection and localization in the aerial images by leveraging oriented bounding boxes to achieve higher localization accuracy compared to applying horizontal bounding boxes. Finally, an error compensation strategy was proposed to eliminate the perspective- and depth-relief-induced localization error by rectifying the oriented bounding boxes obtained from the previous step. Field experiments were conducted to evaluate the method's performance. The results demonstrated its promising accuracy for road user location estimation and its potential to improve the reliability of UAVs in traffic applications.