International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(1): 43-51
Published online March 25, 2020
https://doi.org/10.5391/IJFIS.2020.20.1.43
© The Korean Institute of Intelligent Systems
School of Information Convergence Technology, Daegu University, Gyeongsan, 38453, Korea
Correspondence to :
Seokwon Yeom (yeom@daegu.ac.kr)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Small unmanned aerial vehicles can be effectively used for aerial video surveillance. Although the field of view of the camera mounted on the drone is limited, flying drones can expand their surveillance coverage. In this paper, we address the detection of moving targets in urban environments with a moving drone. The drone moves at a constant velocity and captures video clips of moving vehicles such as cars, buses, and bicycles. Moving vehicle detection consists of frame registration and subtraction followed by thresholding, morphological operations and false blob reduction. First, two consecutive frames are registered; the coordinates of the next frame are compensated by a displacement vector that minimizes the sum of absolute difference between the two frames. Second, the next compensated frame is subtracted from the current frame, and the binary image is generated by thresholding. Finally, morphological operations and false alarm removal extract the target blobs. In the experiments, the drone flies at a constant speed of 5.1 m/s at an altitude of 150 m while capturing video clips of nine moving targets. The detection and false alarm rates as well as the receiver operating characteristic curves are obtained, and the drone velocities in the
Keywords: Drone, Unmanned aerial vehicle, Frame registration, Object detection, Velocity estimation
E-mail: qaws0040@daegu.ac.kr
E-mail: yeom@daegu.ac.kr
International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(1): 43-51
Published online March 25, 2020 https://doi.org/10.5391/IJFIS.2020.20.1.43
Copyright © The Korean Institute of Intelligent Systems.
School of Information Convergence Technology, Daegu University, Gyeongsan, 38453, Korea
Correspondence to:Seokwon Yeom (yeom@daegu.ac.kr)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Small unmanned aerial vehicles can be effectively used for aerial video surveillance. Although the field of view of the camera mounted on the drone is limited, flying drones can expand their surveillance coverage. In this paper, we address the detection of moving targets in urban environments with a moving drone. The drone moves at a constant velocity and captures video clips of moving vehicles such as cars, buses, and bicycles. Moving vehicle detection consists of frame registration and subtraction followed by thresholding, morphological operations and false blob reduction. First, two consecutive frames are registered; the coordinates of the next frame are compensated by a displacement vector that minimizes the sum of absolute difference between the two frames. Second, the next compensated frame is subtracted from the current frame, and the binary image is generated by thresholding. Finally, morphological operations and false alarm removal extract the target blobs. In the experiments, the drone flies at a constant speed of 5.1 m/s at an altitude of 150 m while capturing video clips of nine moving targets. The detection and false alarm rates as well as the receiver operating characteristic curves are obtained, and the drone velocities in the
Keywords: Drone, Unmanned aerial vehicle, Frame registration, Object detection, Velocity estimation
Illustration of a flying drone for detection of moving targets.
Block diagram of moving object detection.
(a) Targets 1–4 at the first frame, (b) Targets 1–6 at the 54th frame, and (c) Targets 1–3 and 6–9 at the 131st frame.
Detection process of (a)
Detection results (bounding boxes) of (a)
ROC curve for target detection.
All detections including false alarms on the expanded coverage with varying
(a) Velocity in the
Table 1 . Target characteristics.
Target ID | Initial frame | Final frame | Direction | Component |
---|---|---|---|---|
1 | 1 | 454 | Right | Blue Bus |
2 | 1 | 454 | Right | Black Sedan |
3 | 1 | 454 | Right | White Sedan |
4 | 1 | 196 | Upward | White Sedan |
5 | 31 | 229 | Upward | Bicycle |
6 | 130 | 208 | Left | Gray Sedan |
7 | 211 | 454 | Left | Blue Bus |
8 | 295 | 454 | Left | White-black Sedan |
9 | 370 | 415 | Parking | White Sedan |
Table 2 . Detection rates.
Target ID | Number of appearances | |||
---|---|---|---|---|
380 | 400 | 420 | ||
1 | 151 | 0.94 | 0.85 | 0.82 |
2 | 151 | 1 | 1 | 1 |
3 | 151 | 1 | 1 | 1 |
4 | 66 | 1 | 1 | 1 |
5 | 67 | 0.92 | 0.83 | 0.77 |
6 | 27 | 1 | 0.96 | 0.96 |
7 | 81 | 1 | 0.82 | 0.66 |
8 | 53 | 1 | 1 | 1 |
9 | 16 | 0.75 | 0.62 | 0.56 |
Avg. | 84 | 0.97 | 0.92 | 0.90 |
Table 3 . Number of false alarms and false alarm rates.
380 | 400 | 420 | |
---|---|---|---|
Number of false alarms | 76 | 23 | 9 |
Number of effective false alarms | 12 | 7 | 4 |
0.50 | 0.15 | 0.06 | |
0.08 | 0.046 | 0.03 |
Table 4 . Speed RMSE.
RMSE (m/s) | |
---|---|
Instant | 0.40 |
Cumulative | 0.07 |
Laily Nur Qomariyati, Nurul Jannah, Suryo Adhi Wibowo, and Thomhert Suprapto Siadari
International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 194-202 https://doi.org/10.5391/IJFIS.2024.24.3.194Dheo Prasetyo Nugroho, Sigit Widiyanto, and Dini Tri Wardani
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International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3): 182-189 https://doi.org/10.5391/IJFIS.2018.18.3.182Illustration of a flying drone for detection of moving targets.
|@|~(^,^)~|@|Block diagram of moving object detection.
|@|~(^,^)~|@|(a) Targets 1–4 at the first frame, (b) Targets 1–6 at the 54th frame, and (c) Targets 1–3 and 6–9 at the 131st frame.
|@|~(^,^)~|@|Detection process of (a)
Detection results (bounding boxes) of (a)
ROC curve for target detection.
|@|~(^,^)~|@|All detections including false alarms on the expanded coverage with varying
(a) Velocity in the