@phdthesis{oai:kyutech.repo.nii.ac.jp:00006272,
 author = {Tangsuksant,  Watcharin},
 month = {2019-12-10},
 note = {1. Introduction||2. Previous Studies||3. Suitable Viewpoint Definition of Waiting for the Bus||4. Classification of Viewpoints while Waiting for the Bus in Situation of Non-Congested Traffic||5. Classification of Viewpoints while Waiting for the Bus in Situation of Congested Traffic||6. Obstacle Detection along the Road||7 Conclusions and Future Work, The bus identification using smartphone camera is one useful application for blind people who travel independently in daily life. Although, many existing researches have focused on the bus identification by using image processing, those researches did not concern the viewpoint of image before the oncoming bus appear in the image. This research proposes the definition and classification of suitable viewpoint of bus-waiting for aiding blind people, which is proposed into three conditions as following: 1） non-congested traffic; 2） congested traffic; and 3） obstacles detection along the road. The first condition of non-congested traffic classified the viewpoint using the road area consideration, which this research applied the Rotational Invariant of Uniform of Local Binary Pattern technique for extracting the road area, and the Back-propagation of Artificial Neural Network for the viewpoint classification. The second condition is congested traffic, which appears a huge number of car in the image. The distribution of car in the image was calculated by many features, which the optimized results showed that seventeen selected features and Random Forest classifier provided the high performance. For third condition, the obstacles along the road will be consideration in case of non-congested traffic. This research combined many existing techniques of image processing to detect the obstacles along the road, which consisted of two main processes: 1） obstacle’s location detection and 2） obstacle’s height estimation. The proposed detected technique can implement in the daylight condition with high performance. According to experimental results, the high performance have shown by 98.56%, 86.00% for non-congested and congested traffics, respectively. Moreover, the performance of obstacles position detection and height estimation were shown by 91.20%, 86.00%, respectively. Based on these results, these are feasible to apply for viewpoint classification in order to assist blind people, who are independently waiting for the bus., 九州工業大学博士学位論文 学位記番号:生工博甲第356号 学位授与年月日:令和元年9月20日, 令和元年度},
 school = {九州工業大学},
 title = {Image-based Viewpoint Classification Related to the Bus-Waiting for Assisting the Blind},
 year = {}
}