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International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3): 182-189

Published online September 25, 2018

https://doi.org/10.5391/IJFIS.2018.18.3.182

© The Korean Institute of Intelligent Systems

Detection and Tracking of Multiple Moving Vehicles with a UAV

Min-Hyuck Lee, and Seokwon Yeom

School of Computer and Communication Engineering, Daegu University, Gyeongsan, Korea

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

Received: June 1, 2018; Revised: September 4, 2018; Accepted: September 4, 2018

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 non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Multiple object detection and tracking are essential for video surveillance. A drone or unmanned aerial vehicle (UAV) is very useful for aerial surveillance due to the efficiency of capturing remote scenes. This paper addresses the detection and tracking of moving vehicles with a UAV. A UAV can capture video sequences above the road at a distance. The detection step consists of frame differencing followed by thresholding, morphological filtering, and removing false alarms considering the true size of vehicles. The centroids of the detected areas are considered as measurements for tracking. Tracking is performed with Kalman filtering to estimate the state of the target based on the dynamic state model and measured positions. In the experiment, three moving cars are captured at a long distance by a drone. Experimental results show that the proposed method well detects moving cars and achieves good accuracy in tracking their dynamic state.

Keywords: UAV/drone imaging, Object detection, Multiple target tracking, Kalman filtering

No potential conflict of interest relevant to this article was reported.

Minhyuck Lee is currently pursuing a B.S. degree in School of Computer and Communication Engineering 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 target tracking.

E-mail: dool0331@daegu.ac.kr


Seokwon Yeom received the M.S. and B.S. degrees in Electronics Engineering from Korea University and the Ph.D. degree in Electrical and Computer Engineering from the University of Connecticut. He is currently a professor of School of Computer and Communication Engineering at Daegu University in Korea. He is now performing several research projects related to image and signal processing. His research interests include image processing, target tracking, machine learning, and optical information processing.

E-mail: yeom@daegu.ac.kr


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3): 182-189

Published online September 25, 2018 https://doi.org/10.5391/IJFIS.2018.18.3.182

Copyright © The Korean Institute of Intelligent Systems.

Detection and Tracking of Multiple Moving Vehicles with a UAV

Min-Hyuck Lee, and Seokwon Yeom

School of Computer and Communication Engineering, Daegu University, Gyeongsan, Korea

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

Received: June 1, 2018; Revised: September 4, 2018; Accepted: September 4, 2018

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 non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multiple object detection and tracking are essential for video surveillance. A drone or unmanned aerial vehicle (UAV) is very useful for aerial surveillance due to the efficiency of capturing remote scenes. This paper addresses the detection and tracking of moving vehicles with a UAV. A UAV can capture video sequences above the road at a distance. The detection step consists of frame differencing followed by thresholding, morphological filtering, and removing false alarms considering the true size of vehicles. The centroids of the detected areas are considered as measurements for tracking. Tracking is performed with Kalman filtering to estimate the state of the target based on the dynamic state model and measured positions. In the experiment, three moving cars are captured at a long distance by a drone. Experimental results show that the proposed method well detects moving cars and achieves good accuracy in tracking their dynamic state.

Keywords: UAV/drone imaging, Object detection, Multiple target tracking, Kalman filtering

Fig 1.

Figure 1.

Block diagram of moving object detection.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 2.

Figure 2.

(a) Cars 1 and 2 at the first frame; (b) Cars 13 at the 150th frame; (c) Car 3 at the 300th frame. Moving cars are indicated by the red circles.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 3.

Figure 3.

(a) Frame differencing; (b) thresholding; (c) morphological filtering and centroids; (d) segmented region by ROI windows after false alarm removal.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 4.

Figure 4.

Measurements including false alarms for 368 frames.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 5.

Figure 5.

Tracking results whenΔ = 0.033 s: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 6.

Figure 6.

Ground truth of position: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 7.

Figure 7.

Approximated ground truth of velocity: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 8.

Figure 8.

Position error: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 9.

Figure 9.

Velocity error: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 10.

Figure 10.

Tracking results whenΔ = 0.1 s: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Fig 11.

Figure 11.

Tracking results whenΔ = 0.2 s: (a) Car 1, (b) Car 2, (c) Car 3.

The International Journal of Fuzzy Logic and Intelligent Systems 2018; 18: 182-189https://doi.org/10.5391/IJFIS.2018.18.3.182

Table 1 . Detection rates.

Number of framesNumber of detectionsDetection rate (%)
Car 1151151100
Car 228528298.95
Car 329927391.3
Total/average73570696.05

Table 2 . Position RMSE.

Sampling time (s)RMSE (m)Average
Car 1Car 2Car 3
0.0330.8851.6191.5671.345
0.10.891.6841.5611.378
0.20.9251.6861.6361.416

Table 3 . Velocity RMSE.

Sampling time (s)RMSE (m/s)Average
Car 1Car 2Car 3
0.0331.4721.712.1141.765
0.11.3771.9831.8441.734
0.21.2872.0152.3661.89

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