{"id":"https://openalex.org/W3009431188","doi":"https://doi.org/10.1109/gcce46687.2019.9015353","title":"An Evaluation Method of Obstacle Avoidance Behavior on Bicycle Trip Using Rider's Gesture","display_name":"An Evaluation Method of Obstacle Avoidance Behavior on Bicycle Trip Using Rider's Gesture","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3009431188","doi":"https://doi.org/10.1109/gcce46687.2019.9015353","mag":"3009431188"},"language":"en","primary_location":{"id":"doi:10.1109/gcce46687.2019.9015353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce46687.2019.9015353","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112654673","display_name":"Masahiro Yagi","orcid":null},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Yagi","raw_affiliation_strings":["Graduate School of Engineering, Hokkaido University, N-13,W-8, Kita-ku, Hokkaido, Sapporo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Hokkaido University, N-13,W-8, Kita-ku, Hokkaido, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073197671","display_name":"Sho Takahashi","orcid":"https://orcid.org/0000-0002-5338-5990"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sho Takahashi","raw_affiliation_strings":["Faculty of Engineering, Hokkaido University, N-13,W-8, Kita-ku, Hokkaido, Sapporo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Hokkaido University, N-13,W-8, Kita-ku, Hokkaido, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113963450","display_name":"Toru Hagiwara","orcid":null},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toru Hagiwara","raw_affiliation_strings":["Faculty of Engineering, Hokkaido University, N-13,W-8, Kita-ku, Hokkaido, Sapporo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Hokkaido University, N-13,W-8, Kita-ku, Hokkaido, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205349734"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"513","last_page":"514"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.8379366397857666},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.8222546577453613},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.7608837485313416},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6739352345466614},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6559421420097351},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5688983798027039},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.4821705222129822},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4576612114906311},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.06970292329788208},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06071650981903076}],"concepts":[{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.8379366397857666},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.8222546577453613},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.7608837485313416},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6739352345466614},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6559421420097351},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5688983798027039},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.4821705222129822},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4576612114906311},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.06970292329788208},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06071650981903076},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.0},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce46687.2019.9015353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce46687.2019.9015353","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2559085405","https://openalex.org/W2592008540","https://openalex.org/W2903820641","https://openalex.org/W4239510810","https://openalex.org/W6733956904"],"related_works":["https://openalex.org/W2930076404","https://openalex.org/W4253519380","https://openalex.org/W2071957557","https://openalex.org/W2596413128","https://openalex.org/W4391249562","https://openalex.org/W2356867392","https://openalex.org/W2913749762","https://openalex.org/W2321940404","https://openalex.org/W2782776446","https://openalex.org/W3043170174"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,23,26,33,39,43,99],"method":[4,89],"for":[5],"evaluation":[6],"of":[7,38,101,108],"obstacle":[8,56,92],"avoidance":[9,57,93],"behavior":[10,48,58,94],"on":[11,20,60,85],"bicycle":[12,24,47],"trips":[13],"in":[14],"videos":[15],"using":[16],"rider's":[17,50,61,69],"gesture":[18,70],"based":[19,59,84],"OpenPose.":[21,75],"Since":[22],"is":[25,29,42],"vehicle":[27],"which":[28],"ridden":[30],"by":[31,73],"rotating":[32],"wheel":[34],"with":[35],"load":[36],"transfer":[37],"rider,":[40],"there":[41],"high":[44],"correlation":[45],"between":[46],"and":[49,95],"gesture.":[51,62],"Therefore,":[52],"we":[53],"evaluate":[54],"the":[55,64,86,91,106],"In":[63,76],"proposed":[65],"method,":[66],"features":[67],"about":[68],"are":[71,82],"obtained":[72],"utilizing":[74],"addition,":[77],"support":[78],"vector":[79],"machines":[80],"(SVMs)":[81],"constructed":[83],"features.":[87],"Our":[88],"detects":[90],"classifies":[96],"that":[97],"to":[98],"degree":[100],"danger.":[102],"Experimental":[103],"results":[104],"show":[105],"effectiveness":[107],"our":[109],"method.":[110]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}