{"id":209814,"created":"2025-01-19T01:11:05.984744+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00209814","sets":["1164:4619:10416:10532"]},"path":["10532"],"owner":"44499","recid":"209814","title":["姿勢推定モデルに基づくスポーツ動画の動作分類"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-02-25"},"_buckets":{"deposit":"ea877a14-2bc4-4e8f-8039-2dbe175f468e"},"_deposit":{"id":"209814","pid":{"type":"depid","value":"209814","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"姿勢推定モデルに基づくスポーツ動画の動作分類","author_link":["529962","529960","529959","529961"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"姿勢推定モデルに基づくスポーツ動画の動作分類"},{"subitem_title":"Leveraging Human Pose Estimation Model for Sports Video Classification","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セッション2-2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-02-25","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"豊橋技術科学大学"},{"subitem_text_value":"豊橋技術科学大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Computer Science and Engineering, Toyohashi University of Technology","subitem_text_language":"en"},{"subitem_text_value":"Computer Science and Engineering, Toyohashi University of Technology ","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/209814/files/IPSJ-CVIM21225016.pdf","label":"IPSJ-CVIM21225016.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM21225016.pdf","filesize":[{"value":"2.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"03e189de-c31f-45be-90d8-f0538118082f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This 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":"佐藤, 荘一朗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"青野, 雅樹"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Soichiro, Sato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaki, Aono","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では，姿勢推定モデルに基づくスポーツ動画の動作分類について述べる．具体的には，PoseNet を用いて 17 種類の人間の骨格の推定座標を出力し，推定座標の推移を表現した特徴量を導入した．これに加えて，骨格の推定座標に基づく動画フレームのクロップを行ったものを従来モデルを拡張した幾つかの DNN モデルへの入力に使用した．この姿勢推定モデルに基づく動作分類のための提案手法を MediaEval2020 Sports Video Classification タスクで提供されたデータに適用した．","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 motion classification method of sports videos based on a posture estimation model. Specifically, we introduced features to estimate the coordinates of 17 types of human skeletons, representing the transition of the estimated coordinates, which in turn were generated by PoseNet. In addition to this, we cropped the video frames based on the estimated coordinates of the skeleton were used as input to several DNN models that extended the conventional models. The proposed method for motion classification based on this posture estimation model was applied to the data provided by the MediaEval2020 Sports Video Classification task.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-02-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"16","bibliographicVolumeNumber":"2021-CVIM-225"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:22:54.098288+00:00","links":{}}