An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos
Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/22/3844 |
| _version_ | 1797547049113092096 |
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| author | Ivan Brkić Mario Miler Marko Ševrović Damir Medak |
| author_facet | Ivan Brkić Mario Miler Marko Ševrović Damir Medak |
| author_sort | Ivan Brkić |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams. |
| first_indexed | 2024-03-10T14:37:51Z |
| format | Article |
| id | doaj.art-5cac9d2924284948a36261550ce2520a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:37:51Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5cac9d2924284948a36261550ce2520a2023-11-20T21:59:47ZengMDPI AGRemote Sensing2072-42922020-11-011222384410.3390/rs12223844An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial VideosIvan Brkić0Mario Miler1Marko Ševrović2Damir Medak3Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, CroatiaChair of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, CroatiaDepartment of Transport Planning, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, CroatiaChair of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, CroatiaUnmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.https://www.mdpi.com/2072-4292/12/22/3844image processingobject detectiontraffic data collectiontraffic flow parametersUAVs |
| spellingShingle | Ivan Brkić Mario Miler Marko Ševrović Damir Medak An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos Remote Sensing image processing object detection traffic data collection traffic flow parameters UAVs |
| title | An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos |
| title_full | An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos |
| title_fullStr | An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos |
| title_full_unstemmed | An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos |
| title_short | An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos |
| title_sort | analytical framework for accurate traffic flow parameter calculation from uav aerial videos |
| topic | image processing object detection traffic data collection traffic flow parameters UAVs |
| url | https://www.mdpi.com/2072-4292/12/22/3844 |
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