Abstract
In recent years, unmanned aerial vehicle (UAV) has become an increasingly popular tool for traffic monitoring and data collection on highways due to its advantage of low cost, high resolution, good flexibility, and wide spatial coverage. Extracting high-resolution vehicle trajectory data from aerial videos taken by a UAV flying over target highway segment becomes a critical research task for traffic flow modeling and analysis. This study aims at proposing a novel methodological framework for automatic and accurate vehicle trajectory extraction from aerial videos. The method starts by developing an ensemble detector to detect vehicles in the target region. Then, the kernelized correlation filter is applied to track vehicles fast and accurately. After that, a mapping algorithm is proposed to transform vehicle positions from the Cartesian coordinates in image to the Frenet coordinates to extract raw vehicle trajectories along the roadway curves. The data denoising is then performed using a wavelet transform to eliminate the biased vehicle trajectory positions. Our method is tested on two aerial videos taken on different urban expressway segments in both peak and non-peak hours on weekdays. The extracted vehicle trajectories are compared with manual calibrated data to testify the framework performance. The experimental results show that the proposed method successfully extracts vehicle trajectories with a high accuracy: the measurement error of Mean Squared Deviation is 2.301 m, the Root-mean-square deviation is 0.175 m, and the Pearson correlation coefficient is 0.999. The video and trajectory data in this study are publicly accessible for serving as benchmark at https://seutraffic.com.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Intelligent Transportation Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.