{"created":"2025-01-19T01:46:33.263048+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00241758","sets":["581:11492:11505"]},"path":["11505"],"owner":"44499","recid":"241758","title":["フィールドスポーツにおける選手個人の同定を用いた追跡手法の開発"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-12-15"},"_buckets":{"deposit":"6dfb61ac-1932-47c4-abd8-ed32eb611a54"},"_deposit":{"id":"241758","pid":{"type":"depid","value":"241758","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"フィールドスポーツにおける選手個人の同定を用いた追跡手法の開発","author_link":["666116","666117","666113","666112","666115","666126","666119","666123","666114","666122","666125","666121","666124","666127","666120","666118"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"フィールドスポーツにおける選手個人の同定を用いた追跡手法の開発"},{"subitem_title":"Development of Tracking Method with Re-identifying Players in Field Sports","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:エンタテインメントコンピューティング] フィールドスポーツ，選手オクルージョン，選手トラッキング，深層距離学習，人物同定","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2024-12-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"関西大学大学院総合情報学研究科"},{"subitem_text_value":"大阪産業大学工学部"},{"subitem_text_value":"関西大学環境都市工学部"},{"subitem_text_value":"（元）関西大学先端科学技術推進機構"},{"subitem_text_value":"公立小松大学生産システム科学部"},{"subitem_text_value":"大阪経済大学情報社会学部"},{"subitem_text_value":"関西大学先端科学技術推進機構"},{"subitem_text_value":"関西大学総合情報学部"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics, Kansai University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Engineering, Osaka Sangyo University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Environmental and Urban Engineering, Kansai University","subitem_text_language":"en"},{"subitem_text_value":"Former Organization for Research and Development of Innovative Science and Technology, Kansai University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Production Systems Engineering and Sciences, Komatsu University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Information Technology and Social Sciences, Osaka University of Economics","subitem_text_language":"en"},{"subitem_text_value":"Organization for Research and Development of Innovative Science and Technology, Kansai University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Informatics, Kansai University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing 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智葳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"姜, 文渊"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 雄平"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, ちひろ"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"坂本, 一磨"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 健二"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鳴尾, 丈司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 成典"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Zhiwei, Xiao","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Wenyuan, Jiang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuhei, Yamamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Chihiro, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuma, Sakamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenji, Nakamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takeshi, Naruo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shigenori, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，深層学習の著しい発展により，カメラ映像から物体追跡の精度が飛躍的に向上した．しかし，長時間にわたって多数の人物を追跡するには，既存手法ではオクルージョンにより追跡が失敗するという課題がある．この課題を解消するため，オクルージョン箇所において人物の軌跡が分断されたことで生成されるtracklet（短時間において正確に追跡した短い軌跡）と，対象人物とを再度同定する手法が提案されている．しかし，オクルージョンによる身体部位の遮蔽の度合いや，姿勢と向きより生じる人物画像の変化に対応できず，人物同定が安定しないことが分かっている．そこで，本研究では，深層距離学習を用いて選手個人の画像群から特徴空間上の重心を算出し，trackletにおけるすべての選手画像を考慮した人物同定および追跡手法を提案し実装する．実証実験から，選手個人の追跡精度が画期的に向上したことにより，フィールドスポーツにおける選手追跡結果を素早く作成できることを確認した．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Rapid development of deep learning in recent years has led to improved tracking accuracy of objects in video. However, existing methods of multiple object tracking face challenges in long-term tracking due to occlusion. To address this problem, tracking algorithms that split trajectories where occlusion occurs have been proposed for generating tracklets, short trajectories accurately tracked in a short time, re-identified as target individuals. Nevertheless, handling variations in the degree of body part occlusion and the dispersion of individual images due to changes in posture and orientation is difficult in these approaches and it leads to unstable identification results. In this study, the authors develop a method utilizing deep metric learning to calculate the centroid in the feature space of individual player image sets. This approach considers all player images in the tracklet for identification and tracking, demonstrating a significantly high improvement in the tracking accuracy of individual players. This method confirms the efficient generation of player tracking results in field sports.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1787","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1775","bibliographicIssueDates":{"bibliographicIssueDate":"2024-12-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"65"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00241636","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":241758,"updated":"2025-01-19T07:32:25.630639+00:00","links":{}}