{"created":"2025-01-18T23:38:38.770007+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00088204","sets":["1164:4619:6988:6990"]},"path":["6990"],"owner":"11","recid":"88204","title":["人検出のための動的顕著性マップモデルの構築"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-01-16"},"_buckets":{"deposit":"0555b8d9-9c6b-40c1-8243-54cb572e3829"},"_deposit":{"id":"88204","pid":{"type":"depid","value":"88204","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"人検出のための動的顕著性マップモデルの構築","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"人検出のための動的顕著性マップモデルの構築"},{"subitem_title":"A Model of Dynamic Saliency for Human Detection","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2013-01-16","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"神戸大学"},{"subitem_text_value":"神戸大学"},{"subitem_text_value":"神戸大学"},{"subitem_text_value":"神戸大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Organization of Advanced Science and Technology, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of System Informatics, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Organization of Advanced Science and Technology, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of System Informatics, Kobe University","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/88204/files/IPSJ-CVIM13185029.pdf"},"date":[{"dateType":"Available","dateValue":"2100-01-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM13185029.pdf","filesize":[{"value":"1.9 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c984558d-97fc-4693-986a-46df0b333816","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":"高柳, 陽平"},{"creatorName":"小篠, 裕子"},{"creatorName":"榎並, 直子"},{"creatorName":"有木, 康雄"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Youhei, Takayanagi","creatorNameLang":"en"},{"creatorName":"Yuko, Ozasa","creatorNameLang":"en"},{"creatorName":"Naoko, Enami","creatorNameLang":"en"},{"creatorName":"Yasuo, Ariki","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":"未学習背景下での動画像からの人検出精度向上のため，動的顕著性マップモデルの構築手法を提案する．顕著性マップは画像中における人の視覚的注意を引く領域を抽出するが，対象のシーンにより有効な顕著』性モデルは異なる．本研究では，静的特徴マップに加えて形状変化量を動的特徴量として抽出し，動的特徴マップから人検出に適した動的顕著性マップモデルを表現する．次に，Adaboostによってアピアランス特徴であるHOG特徴と動的顕著性マップからそれぞれ識別器を構築し，顕著性の高い特徴量を選択を可能とする．提案手法の有効性を確認するため，未学習背景下の動画像を用いて，従来の顕著性モデルとの比較を行った結果，提案手法は検出率となった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose a model of dynamic saliency map for human detection. The saliency map is a model which estimates the bottom-up visual attention in images. However, an effective saliency map model depends on the target scenes. In our method, dynamic saliency map model for human detection is generated from the dynamic feature maps. In the second stage, selection of the high feature of saliency can be achieved by Adaboost. The effectiveness is evaluated with a images captured in real environments.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","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":"2013-01-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"29","bibliographicVolumeNumber":"2013-CVIM-185"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":88204,"updated":"2025-01-21T16:54:55.839232+00:00","links":{}}