{"id":"https://openalex.org/W4281748972","doi":"https://doi.org/10.1145/3523286.3524546","title":"Research and Implementation of Recognition Algorithm of Long-distance Runners Based on Deep Learning","display_name":"Research and Implementation of Recognition Algorithm of Long-distance Runners Based on Deep Learning","publication_year":2022,"publication_date":"2022-01-21","ids":{"openalex":"https://openalex.org/W4281748972","doi":"https://doi.org/10.1145/3523286.3524546"},"language":"en","primary_location":{"id":"doi:10.1145/3523286.3524546","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523286.3524546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","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/A5101702027","display_name":"Yiheng Chen","orcid":"https://orcid.org/0000-0003-2359-3104"},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"YiHeng Chen","raw_affiliation_strings":["School of Computer and Information Engineering, Xiamen University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Xiamen University of Technology, China","institution_ids":["https://openalex.org/I75867142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101177521","display_name":"Huarong Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I75867142","display_name":"Xiamen University of Technology","ror":"https://ror.org/01285e189","country_code":"CN","type":"education","lineage":["https://openalex.org/I75867142"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"HuaRong Xu","raw_affiliation_strings":["School of Computer and Information Engineering, Xiamen University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Xiamen University of Technology, China","institution_ids":["https://openalex.org/I75867142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101702027"],"corresponding_institution_ids":["https://openalex.org/I75867142"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0428252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"229","last_page":"234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9955999851226807,"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":0.9955999851226807,"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":0.9955000281333923,"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":0.9886000156402588,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7713266611099243},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.727774441242218},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6797116994857788},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48100027441978455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4542456567287445},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.4436955153942108},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4105561673641205},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36187905073165894},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0693296492099762}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7713266611099243},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.727774441242218},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6797116994857788},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48100027441978455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4542456567287445},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.4436955153942108},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4105561673641205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36187905073165894},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0693296492099762},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3523286.3524546","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523286.3524546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 2nd International Conference on Bioinformatics and Intelligent Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W4240153047","https://openalex.org/W4249970585"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2522537526","https://openalex.org/W4308659218","https://openalex.org/W2998228095","https://openalex.org/W2791535170","https://openalex.org/W2001271057","https://openalex.org/W2468641972","https://openalex.org/W1586536980","https://openalex.org/W2627333571","https://openalex.org/W4380075502"],"abstract_inverted_index":{"The":[0],"recognition":[1,56,83,97,184],"of":[2,27,30,40,44,77,85,161,171,186],"long-distance":[3,11,18,31,54,78,100,172,187],"runners":[4,101],"is":[5],"mainly":[6],"used":[7],"to":[8,23,52,142],"retrieve":[9],"specific":[10],"runner":[12,55],"targets":[13],"across":[14],"video":[15],"equipment":[16],"in":[17,59,158,182],"running":[19,32],"events,":[20],"which":[21,118],"helps":[22],"improve":[24,168],"the":[25,28,37,74,82,86,109,113,121,126,137,148,159,169,183],"efficiency":[26],"management":[29],"events.":[33],"At":[34],"present,":[35],"with":[36],"rapid":[38],"development":[39],"artificial":[41],"intelligence,":[42],"lots":[43],"scholars":[45],"utilize":[46],"deep":[47,106],"learning-based":[48],"Person":[49],"Re-Identification(Re-ID)":[50],"technology":[51],"achieve":[53,152],"tasks.":[57],"However,":[58],"practical":[60],"applications,":[61],"problems":[62],"such":[63],"as":[64],"occlusion,":[65],"noise,":[66],"brightness":[67],"changes,":[68],"and":[69,129,139,155,163,166],"color":[70],"shifts":[71],"usually":[72],"affect":[73],"collected":[75],"images":[76],"runners,":[79],"thereby":[80],"reducing":[81],"accuracy":[84,170],"existing":[87],"Re-ID":[88],"technology.":[89],"For":[90],"this":[91,93],"reason,":[92],"paper":[94],"proposes":[95],"a":[96],"network":[98,115,144,149],"for":[99],"named":[102],"Ldrr-net":[103,178],"based":[104],"on":[105],"learning.Ldrr-net":[107],"introduces":[108],"IGBN":[110],"structure":[111],"into":[112],"backbone":[114],"called":[116],"Resnet50,":[117],"can":[119,150],"reduce":[120],"adverse":[122],"effects":[123],"caused":[124],"by":[125],"captured":[127],"images,":[128],"has":[130,179],"stronger":[131],"robustness.":[132],"In":[133],"addition,":[134],"we":[135],"modify":[136],"loss,":[138],"propose":[140],"Ldrr-loss":[141],"train":[143],"parameters,":[145],"so":[146],"that":[147,177],"better":[151],"intra-class":[153],"aggregation":[154],"inter-class":[156],"separation":[157],"case":[160],"occlusion":[162],"similar":[164],"features,":[165],"further":[167],"runners'":[173],"recognition.":[174],"Experiments":[175],"show":[176],"certain":[180],"advantages":[181],"task":[185],"runners.":[188]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}