@article{oai:kyutech.repo.nii.ac.jp:00004692,
 author = {Jung, Heewook and Tan, Joo kooi and タン, ジュークイ and Kim, Hyungseop and 金, 亨燮 and 森江, 隆 and Morie, Takashi and Ishikawa, Seiji and 石川, 聖二},
 issue = {1},
 journal = {International Journal of Biomedical Soft Computing and Human Sciences},
 month = {May},
 note = {Detection of a human on a bicycle is an important research subject in an advanced safety vehicle driving system to decrease traffic accidents. The Histograms of Oriented Gradients (HOG) feature has been proposed as useful feature for detecting a standing human in various kinds of background. So, many researchers use currently the HOG feature to detect a human. Detecting a human on a bicycle is more difficult than detecting a standing human, because the appearance of a bicycle can change dramatically according to viewpoints. In this paper, we propose a method of detecting a human on a bicycle using HOG feature and RealAdaBoost algorithm. When detecting a human on a bicycle, occlusion is a cause of decreasing detection efficiency. Occlusion is a serious problem in car vision research, because there are often occlusion in real transportation environment. In such a case, the proposed method predicts the next position of a human on a bicycle using a tracking strategy. Experimental results and their evaluation show satisfactory performance of the proposed method.},
 pages = {33--41},
 title = {Detection and tracking of a human on a bicycle using HOG feature and particle filter},
 volume = {19},
 year = {2013},
 yomi = {モリエ, タカシ and イシカワ, セイジ}
}