{"id":77288,"created":"2025-01-18T23:32:55.719877+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00077288","sets":["1164:4619:6274:6521"]},"path":["6521"],"owner":"10","recid":"77288","title":["環境への自動適応を伴うアピアランスベース頭部姿勢推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2011-08-29"},"_buckets":{"deposit":"f3d0c3f9-f012-46f3-8954-8f22b4ca6fe4"},"_deposit":{"id":"77288","pid":{"type":"depid","value":"77288","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"環境への自動適応を伴うアピアランスベース頭部姿勢推定","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"環境への自動適応を伴うアピアランスベース頭部姿勢推定"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"テーマセッション3","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2011-08-29","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Institute of Industrial Science, The University of 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Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Institute of Industrial Science, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Institute of Industrial Science, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Informatics","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/77288/files/IPSJ-CVIM11178014.pdf"},"date":[{"dateType":"Available","dateValue":"2013-08-29"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM11178014.pdf","filesize":[{"value":"1.2 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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":"本稿では、アピアランスベース頭部姿勢推定における、推定対象環境への自動適応手法を提案する。アピアランスベースの推定手法は、ラベル付けされた正解学習データを推定器の学習時に必要とする。頭部領域の見えは撮影環境によって大きく変化するため、この学習データは推定対象となるテストデータと同じ環境で獲得されることが望ましいが、全ての設置環境で正解学習データを集めることは現実的には不可能な場合が多い。提案手法ではこの問題を解決するために、テスト映像中の歩行者の追跡結果を利用する。人物が進行方向を向いているという仮定のもとで姿勢ラベル付きの頭部画像を獲得し、頭部姿勢クラスの分類器を学習することで、自動的に対象環境に適した頭部姿勢推定器を構築することが可能になる。複数の分類アルゴリズムを使った実験を通して、テスト環境とは異なる環境で獲得された学習データを用いる場合に比べて提案手法の分類精度が高くなることを示す。","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","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":"2011-08-29","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"14","bibliographicVolumeNumber":"2011-CVIM-178"}]},"relation_version_is_last":true,"weko_creator_id":"10"},"updated":"2025-01-21T20:59:05.068117+00:00","links":{}}