{"id":"https://openalex.org/W4416250539","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229397","title":"Multi-Object Pedestrian Tracking Based on YOLO-RED and Hybrid-SORT","display_name":"Multi-Object Pedestrian Tracking Based on YOLO-RED and Hybrid-SORT","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250539","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229397"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229397","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100403704","display_name":"Kan Wang","orcid":"https://orcid.org/0000-0001-5688-6937"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I2802624667","display_name":"Hefei Institutes of Physical Science","ror":"https://ror.org/046n57345","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I2802624667"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kai Wang","raw_affiliation_strings":["Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China,230031"],"affiliations":[{"raw_affiliation_string":"Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China,230031","institution_ids":["https://openalex.org/I2802624667","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370600","display_name":"Jian Wang","orcid":"https://orcid.org/0009-0009-4200-2868"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I2802624667","display_name":"Hefei Institutes of Physical Science","ror":"https://ror.org/046n57345","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I2802624667"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China,230031"],"affiliations":[{"raw_affiliation_string":"Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China,230031","institution_ids":["https://openalex.org/I2802624667","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020976119","display_name":"Huawei Liang","orcid":"https://orcid.org/0000-0003-2508-9606"},"institutions":[{"id":"https://openalex.org/I2802624667","display_name":"Hefei Institutes of Physical Science","ror":"https://ror.org/046n57345","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I2802624667"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huawei Liang","raw_affiliation_strings":["Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China,230031"],"affiliations":[{"raw_affiliation_string":"Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei,China,230031","institution_ids":["https://openalex.org/I2802624667","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100403704"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I2802624667"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37333747,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6341999769210815,"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":0.6341999769210815,"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":0.2296999990940094,"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":0.01640000008046627,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.8529999852180481},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7763000130653381},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6464999914169312},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6065999865531921},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5634999871253967},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5267000198364258},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4684999883174896},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4424000084400177}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8529999852180481},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7763000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7635999917984009},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7457000017166138},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7325999736785889},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6464999914169312},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6065999865531921},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5634999871253967},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5267000198364258},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4424000084400177},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.4009000062942505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.3725999891757965},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C2987395694","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Feature tracking","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229397","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":23,"referenced_works":["https://openalex.org/W2124781496","https://openalex.org/W2252355370","https://openalex.org/W2511791013","https://openalex.org/W2565639579","https://openalex.org/W2603203130","https://openalex.org/W2966926453","https://openalex.org/W2990230185","https://openalex.org/W3034971973","https://openalex.org/W3035694605","https://openalex.org/W3042011474","https://openalex.org/W3086436251","https://openalex.org/W4214507171","https://openalex.org/W4250482878","https://openalex.org/W4286904999","https://openalex.org/W4386076204","https://openalex.org/W4386590375","https://openalex.org/W4388485862","https://openalex.org/W4391307079","https://openalex.org/W4393150006","https://openalex.org/W4393153514","https://openalex.org/W4404001958","https://openalex.org/W4405415639","https://openalex.org/W4405907339"],"related_works":[],"abstract_inverted_index":{"Multi-object":[0],"pedestrian":[1,19,141,164],"tracking":[2,16,142,167],"is":[3,102],"essential":[4],"in":[5,140,162,169],"computer":[6],"vision,":[7],"and":[8,25,112,128,151,166],"the":[9,45,57,62,68,84,110,158],"accuracy":[10,94],"of":[11,47,160,172],"detectors":[12],"significantly":[13],"influences":[14],"overall":[15],"performance.":[17],"Nonetheless,":[18],"detection":[20,37,116,165],"faces":[21],"challenges":[22],"like":[23],"occlusion":[24],"multi-scale":[26,77],"variability.":[27],"To":[28],"address":[29],"these":[30],"issues,":[31],"we":[32,50,66,82],"present":[33],"YOLO-RED,":[34],"an":[35],"enhanced":[36],"model":[38,97],"derived":[39],"from":[40],"YOLOv8.":[41],"Firstly,":[42],"to":[43,91,135],"improve":[44],"ability":[46],"feature":[48,78],"extraction,":[49],"incorporate":[51],"Receptive-Field":[52],"Attention":[53],"Convolution":[54],"(RFAConv)":[55],"into":[56],"C2f":[58],"module,":[59],"hence":[60],"creating":[61],"C2f-RFAConv":[63],"module.":[64],"Secondly,":[65],"introduce":[67],"Efficient":[69],"Multi-Auxiliary":[70],"Feature":[71],"Pyramid":[72],"Network":[73],"(EMAFPN),":[74],"which":[75],"enhances":[76,121],"fusion":[79],"capabilities.":[80],"Finally,":[81],"create":[83],"Dynamic":[85],"Task":[86],"Alignment":[87],"Detection":[88],"Head":[89],"(DTA-Head)":[90],"increase":[92],"localization":[93],"while":[95],"minimizing":[96],"parameters.":[98],"The":[99],"proposed":[100],"method":[101],"systematically":[103],"evaluated":[104],"on":[105],"a":[106,170],"mixed":[107],"dataset":[108],"comprising":[109],"CrowdHuman":[111],"MOT17":[113],"datasets.":[114],"Pedestrian":[115],"experiments":[117],"indicate":[118],"that":[119],"YOLO-RED":[120,137,161],"mAP50":[122],"by":[123,126,132,146,149,153],"1.9%,":[124],"mAP95":[125],"3.0%,":[127],"reduces":[129],"parameter":[130],"size":[131],"18.0%":[133],"compared":[134],"YOLOv8s.":[136],"outperforms":[138],"YOLOv8s":[139],"tasks,":[143],"enhancing":[144,163],"HOTA":[145],"2.1%,":[147],"IDF1":[148],"2.2%,":[150],"MOTA":[152],"3.2%.":[154],"These":[155],"improvements":[156],"demonstrate":[157],"effectiveness":[159],"capabilities":[168],"variety":[171],"real-world":[173],"scenarios.":[174]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
