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Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment
International Journal of Fuzzy Logic and Intelligent Systems 2019;19(1):48-58
Published online March 25, 2019
© 2019 Korean Institute of Intelligent Systems.

Watcharin Tangsuksant, Masashi Noda, Kodai Kitagawa, and Chikamune Wada

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, Japan
Correspondence to: Watcharin Tangsuksant (w.tangsuksant.m@hotmail.com)
Received August 13, 2018; Revised March 11, 2019; Accepted March 13, 2019.
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
To provide an effective notification service for the blinds awaiting the bus, it is crucial to have a viewpoint classification technique in which a viewpoint is defined with tilt and panning of camera. This paper proposes a viewpoint classification method using the car distribution information in the congested traffic environment. The proposed method takes four steps for classification. First, the YOLO algorithm is used to detect the car positions in the images. Second, the car positions are normalized for feature computation. Third, nineteen simple features are extracted and finally, the viewpoint classification is conducted. The proposed method uses the information gain measure to select relevant ones from the extracted features, and uses the Random Forest algorithm as a classifier. In the experiments, the proposed method has been tested for various roadside scenarios of congested traffic in day and night. The accuracies for car detection and viewpoint classification were 79:90% and 86:00%, respectively, which are improved compared to the prior work.
Keywords : Viewpoints classification, Car distribution, Blind people, Congested traffic enverionment