@article{ref1,
title="Automated video-based air traffic surveillance system for counting general aviation aircraft operations at non-towered airports",
journal="Transportation research record",
year="2023",
author="Farhadmanesh, Mohammad and Marković, Nikola and Rashidi, Abbas",
volume="2677",
number="3",
pages="250-273",
abstract="The vast majority of U.S. airports are not equipped with control towers, which limits their ability to keep records of flight operations. Attempts have been made to use sensor-based technologies to count aircraft operations at non-towered airports; however, they exhibit limited accuracy. To this end, we developed an automated video-based surveillance system capable of detecting general aviation aircraft departure and landing operations, which comprise the vast majority of operations at non-towered airports. The proposed computer vision method is comprised of three modules: aircraft detection, aircraft tracking, and operations count and classification. We explored different camera layouts and state-of-the-art machine learning and deep learning methods to determine the best settings to extract operations trajectory features for operations count and classification. The proposed method was tested at five non-towered airports. Integrating deep-neural-network-based aircraft detectors and image-correlation-based aircraft trackers achieved an accuracy of about 95%, while ensuring processing times that are needed for real-time implementation.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/03611981221115087",
url="http://dx.doi.org/10.1177/03611981221115087"
}