{"id":"https://openalex.org/W7124980309","doi":"https://doi.org/10.1109/cvmi66673.2025.11337862","title":"Comparative Study of Object Tracking Algorithms for Urban Intersection Risk Analysis Using CSRT, FairMOT, DeepSORT, and ByteTrack","display_name":"Comparative Study of Object Tracking Algorithms for Urban Intersection Risk Analysis Using CSRT, FairMOT, DeepSORT, and ByteTrack","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W7124980309","doi":"https://doi.org/10.1109/cvmi66673.2025.11337862"},"language":null,"primary_location":{"id":"doi:10.1109/cvmi66673.2025.11337862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi66673.2025.11337862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","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/A5123383697","display_name":"M Muralidharan","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M Muralidharan","raw_affiliation_strings":["School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123380040","display_name":"Yazhiniyan Tamizhnambi","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yazhiniyan Tamizhnambi","raw_affiliation_strings":["School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123423395","display_name":"Advik Chakravarthy Arun","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Advik Chakravarthy Arun","raw_affiliation_strings":["School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123432008","display_name":"Premanand V","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Premanand V","raw_affiliation_strings":["School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, VIT Chennai,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.51500403,"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":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.5045999884605408,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.5045999884605408,"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/T10370","display_name":"Traffic and Road Safety","score":0.15919999778270721,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.09120000153779984,"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/intersection","display_name":"Intersection (aeronautics)","score":0.6833000183105469},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47909998893737793},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.46939998865127563},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4512999951839447},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.44699999690055847},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.41200000047683716},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.39959999918937683},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.34950000047683716}],"concepts":[{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6833000183105469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6269999742507935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4975999891757965},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47909998893737793},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.46939998865127563},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4512999951839447},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3898000121116638},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3377000093460083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3296999931335449},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.3158000111579895},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.30410000681877136},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2994000017642975},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C3018587665","wikidata":"https://www.wikidata.org/wiki/Q7268696","display_name":"Qualitative analysis","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C24404364","wikidata":"https://www.wikidata.org/wiki/Q7246846","display_name":"Probabilistic analysis of algorithms","level":3,"score":0.26600000262260437},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C132835097","wikidata":"https://www.wikidata.org/wiki/Q7663745","display_name":"System safety","level":2,"score":0.25999999046325684}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvmi66673.2025.11337862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi66673.2025.11337862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8291715383529663}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2558899534","https://openalex.org/W2603203130","https://openalex.org/W3084173793","https://openalex.org/W3136121014","https://openalex.org/W3177696923","https://openalex.org/W4286904999","https://openalex.org/W4312473433","https://openalex.org/W4388922867","https://openalex.org/W4390799450","https://openalex.org/W4391687147","https://openalex.org/W4391742000","https://openalex.org/W4399104640","https://openalex.org/W4405584856"],"related_works":[],"abstract_inverted_index":{"Multi":[0],"object":[1],"tracking":[2],"(MOT)":[3],"plays":[4],"a":[5,61,74,140,209],"critical":[6],"role":[7],"in":[8,16,28,100,130],"investigating":[9],"safety":[10,196,214],"and":[11,71,88,98,109,156,165,188,203],"traffic":[12],"dynamics":[13],"at":[14],"intersections":[15],"the":[17,33,179],"city,":[18],"where":[19],"motor":[20],"vehicles,":[21],"bicycles,":[22],"pedestrians":[23],"interact":[24],"with":[25],"one":[26],"another":[27],"complex":[29],"manner.":[30],"Some":[31],"of":[32,64,170,178,194,211],"existing":[34],"research":[35,180],"works":[36],"have":[37],"already":[38],"benchmarked":[39],"MOT":[40,66],"algorithms":[41],"on":[42,50,73,162],"generic":[43],"datasets,":[44],"however,":[45],"limited":[46],"attempt":[47],"is":[48,127],"spent":[49],"their":[51],"specific":[52],"applicability":[53],"to":[54,79,85,143,181,200],"intersection":[55,154],"risk":[56],"analysis.":[57],"This":[58],"paper":[59],"gives":[60,118],"comparative":[62],"analysis":[63,160],"four":[65],"algorithms:":[67],"CSRT,":[68],"DeepSORT,":[69],"FairMOT,":[70],"ByteTrack,":[72],"modular":[75],"YOLOv8":[76,141],"based":[77,161,190],"pipeline":[78,142],"normalize":[80],"detection":[81],"across":[82],"all":[83],"trackers,":[84,191],"enable":[86],"fair":[87],"reproducible":[89],"comparisons.":[90],"Three":[91],"complementary":[92],"datasets":[93],"were":[94,112],"experimented":[95],"(MOT17,":[96],"KITTI":[97],"Cityscapes)":[99],"which":[101],"both":[102],"quantitative":[103],"measures":[104,197],"(MOTA,":[105],"ID":[106],"switches,":[107],"FPS)":[108],"qualitative":[110,158],"observations":[111],"evaluated.":[113],"Findings":[114],"indicate":[115],"that":[116],"ByteTrack":[117],"better":[119],"accurate":[120],"vs.":[121],"realtime":[122],"trade":[123],"off,":[124],"but":[125],"FairMOT":[126],"more":[128,182],"stable":[129],"high":[131],"pedestrian":[132],"count":[133],"scenarios.":[134],"We":[135],"include":[136],"three":[137],"additions:":[138],"1)":[139],"benchmark":[144],"cross-trackers,":[145],"2)":[146],"A":[147],"multi":[148],"dataset":[149],"test":[150],"adjusted":[151],"for":[152],"urban":[153],"scenarios,":[155],"3)":[157],"error":[159],"algorithmic":[163],"strengths":[164],"failure":[166],"cases.":[167],"Further":[168],"extension":[169],"this":[171],"study":[172],"can":[173],"lead":[174],"to,":[175],"an":[176],"expansion":[177],"modern":[183],"trackers":[184],"like":[185,198],"BoTSort,":[186],"OCSort":[187],"transformer":[189],"and;":[192],"adding":[193],"surrogate":[195],"time":[199,206],"collision":[201],"(TTC)":[202],"post":[204],"encroachment":[205],"(PET)":[207],"as":[208],"way":[210],"achieving":[212],"proactive":[213],"prediction.":[215]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-21T00:00:00"}