@article{ref1,
title="Vehicle counting based on vehicle detection and tracking from aerial videos",
journal="Sensors (Basel)",
year="2018",
author="Xiang, Xuezhi and Zhai, Mingliang and Lv, Ning and El Saddik, Abdulmotaleb",
volume="18",
number="8",
pages="s18082560-s18082560",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s18082560",
url="http://dx.doi.org/10.3390/s18082560"
}