{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00094865","sets":["1164:4619:6988:7247"]},"path":["7247"],"owner":"11","recid":"94865","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"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-08-26"},"_buckets":{"deposit":"20ed8e24-4e45-4cea-953e-b0e7fe1506ad"},"_deposit":{"id":"94865","pid":{"type":"depid","value":"94865","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_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","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_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"},{"subitem_title":"Reusing pre-trained classifiers as feature descriptors for adaptation to general dataset","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2013-08-26","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社デンソー"},{"subitem_text_value":"株式会社デンソー"},{"subitem_text_value":"株式会社デンソー"},{"subitem_text_value":"株式会社デンソー"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/94865/files/IPSJ-CVIM13188024.pdf"},"date":[{"dateType":"Available","dateValue":"2100-01-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM13188024.pdf","filesize":[{"value":"2.5 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"64ba76e0-bf1a-4d6c-a003-6f560bb6ad9f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2013 by the Institute of Electronics, Information and Communication Engineers\nThis SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"坂東, 誉司"},{"creatorName":"竹中, 一仁"},{"creatorName":"テヘラニ, ホセイン"},{"creatorName":"酒井, 映"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takashi, Bando","creatorNameLang":"en"},{"creatorName":"Kazuhito, Takenaka","creatorNameLang":"en"},{"creatorName":"Tehrani, Hossein","creatorNameLang":"en"},{"creatorName":"Utsushi, Sakai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2013-08-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"24","bibliographicVolumeNumber":"2013-CVIM-188"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":94865,"updated":"2025-01-21T14:19:52.822913+00:00","links":{},"created":"2025-01-18T23:42:04.842959+00:00"}