The DOI matches its Crossref record.
Vehicle Speed Calculation from Drone Video Based on Deep Learning
Abstract
The number of the vehicles continues to increase from year to year and cause congestion especially when there is a long holiday. The cost of installing and maintaining the recording equipment and supporting infrastructure is quite extortionate. Therefore, the calculation of speed with a flexible and inexpensive cost requires drones. The purpose of this study is to monitor the flow of traffic in areas where there is no supporting infrastructure to install traffic cameras used to calculate the speed of the vehicles during long holidays. The camera shots taken from the drones are used to detect the types of passenger cars using library of You Only Look Once (YOLO) deep learning. The detected object is then tracked by using SORT (Simple Online and Realtime Tracking) to always detect the same object and calculate the speed of the vehicle when passing the RoI (Region of Interest). On the 5, 10, and 15 meters RoI, the lowest error values are 4.79%, 4.38%, and 2.96% respectively. Each of the results was obtained at a height of 10 meters. The proposed method can prove the error calculation of speed vehicle is small when the frame rate (fps) is high, altitude is low and the distance ROI is long.
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“The framework proposed in [77] achieved better results in segmenting the targeted vehicles for thermal images when compared to existing segmentation-based frameworks such as Mask R-CNN detector [102].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [102]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 11
- tsani, 2017, The implementattion of vehicle speed detection using webcam with frame difference method
- prakoso, 2019, Pengembangan Sistem Penghitung Jumlah Kendaraan Memanfaatkan Citra Aerial yang Diambil dengan Pesawat Tanpa Awak
- 10.1109/AVSS.2019.8909903 · DOI · Paper page
- 10.1109/ACCESS.2019.2912200 · DOI · Paper page
- raharjo, 2015, Penggabungan Keputusan Pada Klasifikasi Multi-label Jurnal Ilmiah Teknologi Informasi, 13
- 10.1109/ICSEngT.2013.6650191 · DOI
- miao, 2019, Vehicle direction detection based on YOLOv3 School of Information Engineering Communication University of China
- lu, 2018, A vehicle detection method for aerial image based on yolo Journal of Computer and Communications, 2018, 98
- 10.1109/CENIM.2018.8710854 · DOI
- redmon, 2018, YOLOv3: An Incremental Improvement, arXiv 1804 02767
- 2020, Perkembangan Jumlah Kendaraan Bermotor Menurut Jenis 1949 - 2018
Cited by
Sorted by citations
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 139
“Sim- 660 ilarly, [102] and [103] also used YOLOv3 framework [25] 661 for traffic monitoring tasks, where [102] defined the Region 662 of Interests (RoIs) at the starting and ending points of the 663 monitoring area.”
Method
Metadata
- Authors
- Rahman, T., Siregar, M. A. L., Kurniawan, A., Juniastuti, S., & Yuniarno, E. M.
- Affiliations
- King Mongkut's University of Technology Thonburi (TH)
- Institut Teknologi Sepuluh Nopember (ID)
- Venue
- 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)
- Publisher
- IEEE
- Published
- 2020-11-17
- Pages
- 229-233
- OpenAlex
- W3116856650
- Cited by
- 4
- References
- 0
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 0.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) (publishedVersion)
- Funders
- Institut Teknologi Sepuluh Nopember
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- vehicle speed estimation, drone video, traffic monitoring, YOLO, multi-target tracking, region of interest
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- CoChat. (2026). Citation check: Vehicle Speed Calculation from Drone Video Based on Deep Learning. https://app.cochat.ai/papers/10.1109/cenim51130.2020.9297844
- The paper
- Rahman, T., Siregar, M. A. L., Kurniawan, A., Juniastuti, S., & Yuniarno, E. M. (2020). Vehicle Speed Calculation from Drone Video Based on Deep Learning. 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM). https://doi.org/10.1109/cenim51130.2020.9297844
