@techreport{oai:ipsj.ixsq.nii.ac.jp:00094865,
 author = {坂東, 誉司 and 竹中, 一仁 and テヘラニ, ホセイン and 酒井, 映 and Takashi, Bando and Kazuhito, Takenaka and Tehrani, Hossein and Utsushi, Sakai},
 issue = {24},
 month = {Aug},
 note = {Recently, various advanced driver assistance systems have been developed, e.g., collision-warning system that detect pedestrians from frontal images captured from car mounted camera and calculate collision risk. To construct such object detector, machine-learning approach is frequently employed because of complexity of target object. However, especially in developing process, training dataset for learning the object detector cannot be fixed until final phase of developing. Although it needs quite high cost, we have to iterate gathering images and anno tating object regions to the images for learning the object detector. In this paper, we reused pre-trained pedestrian detectors, which constructed from different dataset respectively, as feature descriptors for adaptation to new dataset. Adapted detectors achieved accurate and robust detection as well as pedestrian detector constructed using the new dataset directly., Recently, various advanced driver assistance systems have been developed, e.g., collision-warning system that detect pedestrians from frontal images captured from car mounted camera and calculate collision risk. To construct such object detector, machine-learning approach is frequently employed because of complexity of target object. However, especially in developing process, training dataset for learning the object detector cannot be fixed until final phase of developing. Although it needs quite high cost, we have to iterate gathering images and anno tating object regions to the images for learning the object detector. In this paper, we reused pre-trained pedestrian detectors, which constructed from different dataset respectively, as feature descriptors for adaptation to new dataset. Adapted detectors achieved accurate and robust detection as well as pedestrian detector constructed using the new dataset directly.},
 title = {識別器の特徴抽出法としての再利用による新規データセットヘの適合法},
 year = {2013}
}