{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214896","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"214896","title":["物体追跡技術の軽量化を目指したモバイルCNNモデルの特徴量抽出の学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"1a4cd54d-a75f-4919-a8de-c7444854e2b7"},"_deposit":{"id":"214896","pid":{"type":"depid","value":"214896","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"物体追跡技術の軽量化を目指したモバイルCNNモデルの特徴量抽出の学習","author_link":["553047","553046","553045"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"物体追跡技術の軽量化を目指したモバイルCNNモデルの特徴量抽出の学習"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","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":"名大"},{"subitem_text_value":"名大"},{"subitem_text_value":"名大"}]},"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/214896/files/IPSJ-Z83-4N-03.pdf","label":"IPSJ-Z83-4N-03.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-4N-03.pdf","filesize":[{"value":"632.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"b1dd0902-03bb-4838-8781-50c6698a0904","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"村手, 翼"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"渡辺, 崇"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山田, 真生"}],"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":"本研究では，一般物体認識CNNを用いた高精度な物体追跡手法を，精度を維持しつつ軽量化かつ高速化することを目的とする. 一般物体認識CNNを用いた物体追跡は，動画像の各フレームにおいて対象と同カテゴリーの領域を認識し，予測領域とする手法である．従来手法ではCNNに学習済VGG16を用いており，高速な演算を行う為にはGPUなどの強力な計算リソースを要する為，計算資源の限られた環境での応用は困難であった．この問題に対してモバイルCNNモデルを応用し，計算リソースが限られた環境下での高速かつ高精度な物体追跡手法の実現を目指す．本研究ではCNNにMobileNetを用いて実験を行う．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"184","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"183","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214896,"updated":"2025-01-19T16:25:02.805118+00:00","links":{},"created":"2025-01-19T01:15:41.307968+00:00"}