Abstract Unmanned aerial vehicle (UAV) attracts increasing attentions in transportation community due to its low cost, wide view coverage, rapid deployment. Previous studies mainly focus on extracting traffic parameters from aerial videos with air-borne camera in planar movements. Less focuses is paid to extract traffic parameters from the UAV-like videos recorded with multi-dimensional camera movement. To address this issue, we propose a adapted framework for obtaining traffic parameters with the UAV camera moving at different dimensions. First, the framework introduces a temporally robust global motion compensation (TRGMC) model to compensate UAV camera movements and obtain a stabilized traffic scenery background. Second, the kernelized correlation filter (KCF) is integrated into the proposed framework to accurately track vehicles. Third, we introduce the Hough line detection to find reference markings in videos and map the image length from video to physical length in real world. Fourth, we estimate microscopic traffic parameters including individual vehicle speed, time headway and space headway in a traffic stream. We testify the proposed framework performance on three different videos which are collected under interference of different camera movements. The experimental results show that the proposed method achieves an accuracy of 96.98% and 96.94%, respectively.
Chen et al. (2025) studied this question.