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Sensors 2018, 18(8), 2560;

Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos

The School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
The school of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Author to whom correspondence should be addressed.
Received: 5 June 2018 / Revised: 31 July 2018 / Accepted: 2 August 2018 / Published: 4 August 2018
(This article belongs to the Section Intelligent Sensors)
Full-Text   |   PDF [6470 KB, uploaded 4 August 2018]   |  


Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively. View Full-Text
Keywords: vehicle counting; unmanned aerial vehicle; vehicle detection; visual tracking; aerial video vehicle counting; unmanned aerial vehicle; vehicle detection; visual tracking; aerial video

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Xiang, X.; Zhai, M.; Lv, N.; El Saddik, A. Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos. Sensors 2018, 18, 2560.

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