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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

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Donho Nam and Seokwon Yeom

School of Information Convergence Technology, Daegu University, Gyeongsan, 38453, Korea

Correspondence to :
Seokwon Yeom (yeom@daegu.ac.kr)

Received: September 17, 2019; Revised: December 24, 2019; Accepted: March 19, 2020

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 x and y directions are estimated by the displacement vector. The average detection rate ranges from 90% to 97% while the false alarm rate ranges from 0.06 to 0.5. The root mean square error of the speed is 0.07 m/s when the reference frame is fixed, showing the robustness of the proposed method.

Keywords: Drone, Unmanned aerial vehicle, Frame registration, Object detection, Velocity estimation

Donho Nam is currently B.S. degree in School of ICT Convergence at Daegu University in South Korea. He has been working on researches related to image processing using UAV. His research interests include intelligent image processing and multiple target detection.

E-mail: qaws0040@daegu.ac.kr


Seokwon Yeom has been a faculty member of Daegu University since 2007. He is now a full professor of the same university, School of ICT Convergence. He was a visiting scholar at the University of Maryland in 2014 and a director of the Gyeongbuk techno-park specialization center in 2013. He has a Ph.D. in Electrical and Computer Engineering from the University of Connecticut in 2006. He is currently working on projects related with smart surveillance of small drones. His research interests are intelligent processing of image and optical information, machine learning, and target tracking. He has researched on multiple target tracking for airborne early warning, three-dimensional image processing with digital holography and integral imaging, photon-counting linear discriminant analysis and nonlinear matched filter, millimeter wave and infrared image analysis, low-resolution object recognition, and aerial surveillance with small unmanned vehicle systems. He is a member of the editorial board of Applied Sciences since 2019, a board member of the Korean Institute of Intelligent Systems since 2016, and a member of the board of directors of the Korean Institute of Convergence Signal Processing since 2014. He has served as program chair of the ICCCS2015, ISIS2017, iFUZZY2018, and ICCCS2019.

E-mail: yeom@daegu.ac.kr


Article

Original Article

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.

Moving Vehicle Detection and Drone Velocity Estimation with a Moving Drone

Donho Nam and Seokwon Yeom

School of Information Convergence Technology, Daegu University, Gyeongsan, 38453, Korea

Correspondence to:Seokwon Yeom (yeom@daegu.ac.kr)

Received: September 17, 2019; Revised: December 24, 2019; Accepted: March 19, 2020

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.

Abstract

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 x and y directions are estimated by the displacement vector. The average detection rate ranges from 90% to 97% while the false alarm rate ranges from 0.06 to 0.5. The root mean square error of the speed is 0.07 m/s when the reference frame is fixed, showing the robustness of the proposed method.

Keywords: Drone, Unmanned aerial vehicle, Frame registration, Object detection, Velocity estimation

Fig 1.

Figure 1.

Illustration of a flying drone for detection of moving targets.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 2.

Figure 2.

Block diagram of moving object detection.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 3.

Figure 3.

(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.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 4.

Figure 4.

Detection process of (a) Figure 3(a), (b)Figure 3(b), and (c)Figure 3(c).

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 5.

Figure 5.

Detection results (bounding boxes) of (a) Figure 3(a), (b)Figure 3(b), and (c)Figure 3(c).

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 6.

Figure 6.

ROC curve for target detection.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 7.

Figure 7.

All detections including false alarms on the expanded coverage with varying θs as (a) 380, (b) 400, and (c) 420.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Fig 8.

Figure 8.

(a) Velocity in the x direction, (b) velocity in the y direction, and (c) speed.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 43-51https://doi.org/10.5391/IJFIS.2020.20.1.43

Table 1 . Target characteristics.

Target IDInitial frameFinal frameDirectionComponent
11454RightBlue Bus
21454RightBlack Sedan
31454RightWhite Sedan
41196UpwardWhite Sedan
531229UpwardBicycle
6130208LeftGray Sedan
7211454LeftBlue Bus
8295454LeftWhite-black Sedan
9370415ParkingWhite Sedan

Table 2 . Detection rates.

Target IDNumber of appearancesθs
380400420
11510.940.850.82
2151111
3151111
466111
5670.920.830.77
62710.960.96
78110.820.66
853111
9160.750.620.56
Avg.840.970.920.90

Table 3 . Number of false alarms and false alarm rates.

θs
380400420
Number of false alarms76239
Number of effective false alarms1274
Rf0.500.150.06
Reff0.080.0460.03

Table 4 . Speed RMSE.

RMSE (m/s)
Instant0.40
Cumulative0.07

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