Abstract
Traffic data collection is the fundamental step in most applications of intelligent transportation systems (ITS). Recently, traffic data collection methods have become more robust and diversified, yet still have some limitations in their flexibility and coverage. Onboard monocular cameras have considerable potential to be turned into cost-effective moving traffic sensors combining the low cost and ego-vehicles’ high mobility. Existing studies have explored the feasibility of onboard cameras for scene understanding, etc. However, few studies have been conducted to utilize onboard monocular cameras for traffic flow data collection. To this end, this paper puts forward a method using the onboard monocular camera to collect traffic data. The basic structure is composed of a you-only-look-once (YOLO) model and spatial transformer network (STN) to detect vehicles in real-time. Then the traffic flow parameters are computed via fundamental optic and traffic flow theories. The experiment results show its reliability and similar sensing accuracy with inductive loop detectors on the road segment detection. In addition, the STN-YOLO model has a higher vehicle detection accuracy than the original YOLO model under complicated conditions.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
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Video data collected by onboard monocular camera on November 3, 2017, and June 3, 2019.
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Loop detector data collected on on November 3, 2017, and June 3, 2019.
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Code of STN-YOLO model
Acknowledgments
This research was fully supported by Smart Transportation Application and Research (STAR) Laboratory, University of Washington.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jun 7, 2019
Accepted: Apr 15, 2020
Published online: Jul 6, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 6, 2020
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