{"updated":"2025-01-19T16:21:33.155205+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215018","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"215018","title":["YOLO及びMOTを用いた車種別交通量調査の自動化のための研究"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"65da6d42-39b3-4a2b-a00c-0ded1eb37bf3"},"_deposit":{"id":"215018","pid":{"type":"depid","value":"215018","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"YOLO及びMOTを用いた車種別交通量調査の自動化のための研究","author_link":["553402","553404","553405","553403"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"YOLO及びMOTを用いた車種別交通量調査の自動化のための研究"}]},"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":"東京高専"},{"subitem_text_value":"東京高専"},{"subitem_text_value":"東京高専"},{"subitem_text_value":"東京高専"}]},"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/215018/files/IPSJ-Z83-7Q-07.pdf","label":"IPSJ-Z83-7Q-07.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-7Q-07.pdf","filesize":[{"value":"542.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"e27ee820-bab2-45a4-bc4b-79a380bca247","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":"Temuulen, Dulbadrakh"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴木, 雅人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北越, 大輔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西村, 亮"}],"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":"交差点を通過する車両を、車種と来た方向によって分類作業を行う交通量調査において，その自動化が検討されている。本稿では，歩道から撮影した動画を用いて交通量調査の自動化を行うため、深層学習アルゴリズムのYOLOを用いて検出した動画内の車両をMulti Object Tracking（MOT）によって追跡する手法を提案する。車両同士の重なり合いが頻発するため、車両の動きの予測に用いられるバウンディングボックスの変化率を固定して計算のノイズを少なくすることによってトラッキング制度の改善し，分類を行った。改善前後のシステムを用いて実験を行い，誤検出と見逃しの観点から精度評価し，その精度向上を確認した。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"438","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"437","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":215018,"created":"2025-01-19T01:15:48.273503+00:00"}