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A platform for research: civil engineering, architecture and urbanism
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.
Highlights UAVs become popular in collecting positional data of road users. Depth relief and perspective distortion impact the road user localization accuracy. An error compensation scheme was proposed to tackle this issue. Results signified the method's performance for accurate road user localization. The method has the potential to promote UAV use in transportation.
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.
Highlights UAVs become popular in collecting positional data of road users. Depth relief and perspective distortion impact the road user localization accuracy. An error compensation scheme was proposed to tackle this issue. Results signified the method's performance for accurate road user localization. The method has the potential to promote UAV use in transportation.
Accurate road user localization in aerial images captured by unmanned aerial vehicles
Lu, Linjun (author) / Dai, Fei (author)
2023-12-23
Article (Journal)
Electronic Resource
English
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