{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205469","sets":["6504:10247:10252"]},"path":["10252"],"owner":"6748","recid":"205469","title":["ポーズデータとNNを用いた動作識別手法の調査"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"54b28bfe-1b8b-48c4-a679-30bf086aedfc"},"_deposit":{"id":"205469","pid":{"type":"depid","value":"205469","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"ポーズデータとNNを用いた動作識別手法の調査","author_link":["509859","509858","509860","509861"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ポーズデータとNNを用いた動作識別手法の調査"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2020-02-20","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大"},{"subitem_text_value":"NII"},{"subitem_text_value":"産総研"},{"subitem_text_value":"お茶の水女子大"}]},"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/205469/files/IPSJ-Z82-5V-01.pdf","label":"IPSJ-Z82-5V-01.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-5V-01.pdf","filesize":[{"value":"474.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"df89b15c-8ca9-4b6a-902a-876aeb3c9467","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高崎, 智香子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹房, あつ子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中田, 秀基"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"センサの発達やクラウドコンピューティングの普及により，一般家庭で取得，蓄積した動画像が子供やお年寄りの見守りサービスや防犯対策，セキュリティに活用されるようになってきた．しかし，家庭のセンサで取得した動画像をリアルタイムに機械学習を用いて解析するにはデータサイズと解析計算量が大きいため，サーバやストレージを用いてデータの分析や蓄積を行う必要がある．本研究では，センサ側で姿勢推定ライブラリ OpenPoseを使って前処理を行い，動画像から特徴量を抽出してデータ量を削減した後，クラウドでその特徴量データを収集して機械学習を用いて動作の識別を行うことで処理遅延，プライバシ，通信コストの問題に対処する．前処理によりもとの動画像に含まれていた情報量が大幅に失われてしまうため，特徴量のみでどの程度の精度で学習や推論ができるのかについて調査した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"134","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"133","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":205469,"updated":"2025-01-19T19:46:35.054423+00:00","links":{},"created":"2025-01-19T01:07:37.158776+00:00"}