{"id":"https://openalex.org/W2999984039","doi":"https://doi.org/10.1109/ivcnz48456.2019.8961035","title":"Track Cyclist Detection and Identification using Mask R-CNN and K-means Clustering","display_name":"Track Cyclist Detection and Identification using Mask R-CNN and K-means Clustering","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W2999984039","doi":"https://doi.org/10.1109/ivcnz48456.2019.8961035","mag":"2999984039"},"language":"en","primary_location":{"id":"doi:10.1109/ivcnz48456.2019.8961035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz48456.2019.8961035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","raw_type":"proceedings-article"},"type":"article","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/A5060820284","display_name":"Marco Tyler-Rodrigue","orcid":null},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Marco Tyler-Rodrigue","raw_affiliation_strings":["Department of Computer Science, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100730175","display_name":"Richard Green","orcid":"https://orcid.org/0000-0002-8671-8966"},"institutions":[{"id":"https://openalex.org/I185492890","display_name":"University of Canterbury","ror":"https://ror.org/03y7q9t39","country_code":"NZ","type":"education","lineage":["https://openalex.org/I185492890"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Richard Green","raw_affiliation_strings":["Department of Computer Science, University of Canterbury, Christchurch, New Zealand"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Canterbury, Christchurch, New Zealand","institution_ids":["https://openalex.org/I185492890"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5060820284"],"corresponding_institution_ids":["https://openalex.org/I185492890"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47120837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"393","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9966999888420105,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9966999888420105,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9900000095367432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7049543857574463},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700674831867218},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6999282240867615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6730124354362488},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6226125955581665},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5553332567214966},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5280514359474182},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.515982449054718},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47943490743637085},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23374274373054504}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7049543857574463},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700674831867218},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6999282240867615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6730124354362488},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6226125955581665},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5553332567214966},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5280514359474182},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.515982449054718},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47943490743637085},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23374274373054504},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivcnz48456.2019.8961035","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz48456.2019.8961035","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1988914571","https://openalex.org/W2090569038","https://openalex.org/W2103827867","https://openalex.org/W2159055566","https://openalex.org/W2465597433","https://openalex.org/W2513907769","https://openalex.org/W2806070179","https://openalex.org/W2936791947","https://openalex.org/W2963150697","https://openalex.org/W6725552064","https://openalex.org/W6761629322"],"related_works":["https://openalex.org/W1542224353","https://openalex.org/W1661087619","https://openalex.org/W2116854923","https://openalex.org/W2750730210","https://openalex.org/W2236974868","https://openalex.org/W4312766348","https://openalex.org/W4233939244","https://openalex.org/W2730764323","https://openalex.org/W3123806511","https://openalex.org/W1976727107"],"abstract_inverted_index":{"Automatically":[0],"identifying":[1,85],"a":[2,11,42,47,53,65,89,94,107,132,146,175],"cyclist":[3,55,63],"as":[4],"they":[5],"pass":[6],"the":[7,39,57,62,117,129,171,185,188,207,213],"starting":[8],"line":[9,59],"in":[10,88,166,190],"track":[12,54],"cycling":[13],"race":[14],"is":[15,187],"primarily":[16],"restricted":[17],"to":[18,23,35,38,49,77,97,127,142,229],"physical":[19,33],"detection":[20],"systems":[21],"assigned":[22],"individual":[24,168],"competitors.":[25],"These":[26],"current":[27],"solutions":[28],"are":[29,139,215],"expensive":[30],"and":[31,60,71,101,124,203,232],"require":[32],"hardware":[34],"be":[36,143,164,210],"attached":[37],"cyclists":[40,87,162,194,208,231],"during":[41,145],"race.":[43,147],"This":[44,74],"paper":[45],"proposes":[46],"method":[48,75,115,149,186],"both":[50],"detect":[51],"when":[52],"passes":[56],"lap":[58],"identify":[61,230],"using":[64,200],"region":[66],"convolutional":[67],"neural":[68,108],"network":[69,109],"(R-CNN)":[70],"K-means":[72,111,125,179,201],"clustering.":[73,112],"aims":[76],"overcome":[78],"several":[79],"limitations":[80],"of":[81,135,155,174,184,240],"prior":[82],"research":[83,219],"by":[84,105],"multiple":[86],"single":[90,95,133,158],"frame,":[91],"requiring":[92],"only":[93],"image":[96,134,159],"train":[98,128],"per":[99],"cyclist,":[100],"increasing":[102],"identification":[103,153,191],"accuracy":[104,154,192],"combining":[106],"with":[110,120,131,157,235],"The":[113,148,181],"proposed":[114],"uses":[116],"Mask":[118,177],"R-CNN":[119,178],"COCO":[121],"2017":[122],"dataset":[123],"clustering":[126,202],"detector":[130],"each":[136],"cyclist.":[137],"Cyclists":[138],"then":[140],"able":[141],"identified":[144],"achieved":[150],"an":[151,167],"average":[152],"95.1%":[156],"training.":[160],"Multiple":[161],"could":[163],"detected":[165],"frame":[169],"through":[170],"successful":[172],"integration":[173],"combined":[176],"detector.":[180],"primary":[182],"limitation":[183],"reduction":[189],"for":[193,238],"wearing":[195],"similar":[196],"coloured":[197],"clothing.":[198],"By":[199],"RGB":[204],"range":[205],"thresholding,":[206],"may":[209],"mis-identified":[211],"if":[212],"colours":[214],"too":[216],"similar.":[217],"Further":[218],"that":[220],"would":[221],"benefit":[222],"this":[223],"solution":[224],"includes":[225],"developing":[226],"additional":[227,233],"points-of-interest":[228],"cameras":[234],"different":[236],"angles":[237],"instances":[239],"total":[241],"overlap":[242],"between":[243],"cyclists.":[244]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
