{"created":"2025-01-19T01:43:08.172165+00:00","updated":"2025-01-19T08:16:08.887549+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00239470","sets":["1164:6390:11456:11717"]},"path":["11717"],"owner":"44499","recid":"239470","title":["骨格推定を用いた作業者の個人識別手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-09-19"},"_buckets":{"deposit":"c09af741-cd4d-4c9b-a111-3f4647fc9d1f"},"_deposit":{"id":"239470","pid":{"type":"depid","value":"239470","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"骨格推定を用いた作業者の個人識別手法の提案","author_link":["656463","656465","656464"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"骨格推定を用いた作業者の個人識別手法の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"認識・識別アプリケーション","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-09-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岩手県立大学院ソフトウェア情報学研究科"},{"subitem_text_value":"岩手県立大学院ソフトウェア情報学研究科"},{"subitem_text_value":"岩手県立大学院ソフトウェア情報学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Software and Information Science, Iwate Prefectural University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Software and Information Science, Iwate Prefectural University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Software and Information Science, Iwate Prefectural 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/239470/files/IPSJ-CDS24041015.pdf","label":"IPSJ-CDS24041015.pdf"},"date":[{"dateType":"Available","dateValue":"2026-09-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CDS24041015.pdf","filesize":[{"value":"11.3 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"47"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"76a59039-d7da-45a3-9cc6-c21f97fb87f7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"工藤, 諒太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"堀川, 三好"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岡本, 東"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628327","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8604","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"製造現場において動画解析により複数の作業者を対象とした行動分析を行う場合，撮影されている人物が誰なのかを個人識別する必要がある．しかしながら，広角撮影による不鮮明な動画を用いることが多く，また，作業者が共通の帽子，作業着およびマスクを着用する場合が多いため，顔認証や衣服等の外観情報に基づく個人識別手法を適用することは困難である．本研究では，この課題を解決するため，作業者の動画から姿勢推定技術により骨格データを抽出し，グラフニューラルネットワークを用いて個人識別する手法を提案する．その際，複数の作業者が同時に撮影される状況を考慮し，センシングデバイスによる測位技術と動画解析による人物追跡を併用することで，個人識別モデル生成のためのアノテーションを簡略化する手法を提案する．本稿では，提案手法の有効性を検証するために学内実験を行った結果について報告をする．結果として，6 名の作業者を想定した場合に，1 時間の作業データを収集し，簡易なアノテーションを行うことで高精度な個人識別モデルが生成可能なことを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンシューマ・デバイス＆システム（CDS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-09-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"15","bibliographicVolumeNumber":"2024-CDS-41"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":239470,"links":{}}