{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00229180","sets":["1164:6757:11095:11361"]},"path":["11361"],"owner":"44499","recid":"229180","title":["複数カメラを用いた確率的な三次元トラッキング手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-11-09"},"_buckets":{"deposit":"650c923a-340f-464b-9210-5a89d4b40005"},"_deposit":{"id":"229180","pid":{"type":"depid","value":"229180","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"複数カメラを用いた確率的な三次元トラッキング手法の提案","author_link":["615838","615839","615840","615841"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"複数カメラを用いた確率的な三次元トラッキング手法の提案"},{"subitem_title":"Probabilistic 3D Tracking Using Multi-Camera","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2023-11-09","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学大学院情報科学研究科"},{"subitem_text_value":"大阪大学サイバーメディアセンター"},{"subitem_text_value":"大阪大学サイバーメディアセンター"},{"subitem_text_value":"大阪大学サイバーメディアセンター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Cybermedia Center, Osaka Uniersity","subitem_text_language":"en"},{"subitem_text_value":"Cybermedia Center, Osaka Uniersity","subitem_text_language":"en"},{"subitem_text_value":"Cybermedia Center, Osaka Uniersity","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/229180/files/IPSJ-DCC23035013.pdf","label":"IPSJ-DCC23035013.pdf"},"date":[{"dateType":"Available","dateValue":"2025-11-09"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DCC23035013.pdf","filesize":[{"value":"4.0 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":"50"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"07614284-d4f2-4347-aa84-020745e06cab","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松田, 脩佑"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"テチャサンティクーン, ナタオン"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大下, 裕一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"下西, 英之"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628338","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":"2188-8868","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"任意の空間において人を表現，理解するためには，その空間におけるある人物を追跡し，そのコンテクストを抽出することが重要である．このためにはまず三次元空間におけるトラッキングが必要となるが，従来研究では深度センサや無線技術を用いた手法が大部分を占めており，現実的なコストと精度を達成できているものは少ない．これらを実現するため，本研究では向きの異なる複数のカメラを用い，三次元空間を確率空間と捉えることで確率的な位置推定を行う．この際課題となる同一物体識別は，マルコフ確率場（MRF）を用いた分類問題として処理する．そして，以上から得られる推定位置の確率分布により，逐次的なベイズ推定を用いたトラッキングを実現する．評価実験では，一般的な計算機において 10fps 程度の処理速度を持つ軽量なモデルを実装し，三次元でのトラッキングでは平均の位置誤差を 27cm に抑えつつ 80％ 程度の MOTA を達成した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告デジタルコンテンツクリエーション（DCC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-11-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"13","bibliographicVolumeNumber":"2023-DCC-35"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":229180,"updated":"2025-01-19T11:36:37.325094+00:00","links":{},"created":"2025-01-19T01:28:16.787828+00:00"}