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http://dx.doi.org/10.12815/kits.2017.16.6.90

GAP Estimation on Arterial Road via Vehicle Labeling of Drone Image  

Jin, Yu-Jin (Dept. of Earth and Environmental System Sciences National Univ. of Pukyong)
Bae, Sang-Hoon (Dept. of Spatial Information Engineering System, National Univ. of Pukyong)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.16, no.6, 2017 , pp. 90-100 More about this Journal
Abstract
The purpose of this study is to detect and label the vehicles using the drone images as a way to overcome the limitation of the existing point and section detection system and vehicle gap estimation on Arterial road. In order to select the appropriate time zone, position, and altitude for the acquisition of the drone image data, the final image data was acquired by shooting under various conditions. The vehicle was detected by applying mixed Gaussian, image binarization and morphology among various image analysis techniques, and the vehicle was labeled by applying Kalman filter. As a result of the labeling rate analysis, it was confirmed that the vehicle labeling rate is 65% by detecting 185 out of 285 vehicles. The gap was calculated by pixel unitization, and the results were verified through comparison and analysis with Daum maps. As a result, the gap error was less than 5m and the mean error was 1.67m with the preceding vehicle and 1.1m with the following vehicle. The gaps estimated in this study can be used as the density of the urban roads and the criteria for judging the service level.
Keywords
Drone; Image Processing; Vehicle Detecting; Vehicle Labeling; Vehicle Gap;
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Times Cited By KSCI : 1  (Citation Analysis)
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