{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00202722","sets":["581:10023:10024"]},"path":["10024"],"owner":"33195","recid":"202722","title":["FlowScan: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-01-15"},"_buckets":{"deposit":"4094bfad-aad3-46e9-8f65-0836c11ca9dd"},"_deposit":{"id":"202722","pid":{"type":"depid","value":"202722","revision_id":0},"owners":[33195],"status":"published","created_by":33195},"item_title":"FlowScan: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning","author_link":["498155","498158","498156","498160","498154","498153","498162","498161","498159","498157"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"FlowScan: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning"},{"subitem_title":"FlowScan: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：活き活きとしたスマートシティを実現する高度交通システムとパーベイシブシステム] deep learning, CNN, LSTM, pedestrian tracking, dashboard cameras","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2020-01-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Faculty of Data Science, Shiga University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"}]},"item_2_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":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Data Science, Shiga University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/202722/files/IPSJ-JNL6101009.pdf","label":"IPSJ-JNL6101009.pdf"},"date":[{"dateType":"Available","dateValue":"2022-01-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6101009.pdf","filesize":[{"value":"4.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"6562f8c9-816e-42dd-bf3d-dc3821cdbeeb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yusuke, Hara"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Hasegawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Uchiyama"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takaaki, Umedu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Teruo, Higashino"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yusuke, Hara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Hasegawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Uchiyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takaaki, Umedu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Teruo, Higashino","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_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":"In this paper, we propose FlowScan: a pedestrian flow estimation technique based on a dashboard camera. Grasping flows of people is important for various purposes such as city planning and event detection. FlowScan can estimate pedestrian flows on sidewalks without taking much cost. Currently, dashboard cameras have been becoming so popular for preserving the evidence of traffic accidents and security reasons. FlowScan assumes that an application which analyzes video from the camera is installed on an on-board device. To realize such an application, we need to design a method for pedestrian recognition and occlusion-proof tracking of pedestrians. For pedestrian recognition, the application uses Deep Learning-based techniques; CNN (Convolutional Neural Networks) and LSTM (Long-Short-Term-Memory). In this process, the faces and backs of their heads are searched in the video separately to detect not only the number of pedestrians but also their directions. Then, a series of detected positions of heads are arranged into tracks depending on the similarity of locations and colors considering the knowledge about the movement of the vehicle and pedestrians. We have evaluated FlowScan using real video data recorded by a dashboard camera. The mean absolute error rate for people flow estimation of both directions was 18.5%, highlighting its effectiveness compared with the state-of-the-art.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.28(2020) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.28.55\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose FlowScan: a pedestrian flow estimation technique based on a dashboard camera. Grasping flows of people is important for various purposes such as city planning and event detection. FlowScan can estimate pedestrian flows on sidewalks without taking much cost. Currently, dashboard cameras have been becoming so popular for preserving the evidence of traffic accidents and security reasons. FlowScan assumes that an application which analyzes video from the camera is installed on an on-board device. To realize such an application, we need to design a method for pedestrian recognition and occlusion-proof tracking of pedestrians. For pedestrian recognition, the application uses Deep Learning-based techniques; CNN (Convolutional Neural Networks) and LSTM (Long-Short-Term-Memory). In this process, the faces and backs of their heads are searched in the video separately to detect not only the number of pedestrians but also their directions. Then, a series of detected positions of heads are arranged into tracks depending on the similarity of locations and colors considering the knowledge about the movement of the vehicle and pedestrians. We have evaluated FlowScan using real video data recorded by a dashboard camera. The mean absolute error rate for people flow estimation of both directions was 18.5%, highlighting its effectiveness compared with the state-of-the-art.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.28(2020) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.28.55\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2020-01-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"61"}]},"relation_version_is_last":true,"weko_creator_id":"33195"},"id":202722,"updated":"2025-01-19T20:47:23.555608+00:00","links":{},"created":"2025-01-19T01:05:16.260828+00:00"}