{"id":"https://openalex.org/W4401725938","doi":"https://doi.org/10.1109/tiv.2024.3446851","title":"Convolutional Unscented Kalman Filter for Multi-Object Tracking With Outliers","display_name":"Convolutional Unscented Kalman Filter for Multi-Object Tracking With Outliers","publication_year":2024,"publication_date":"2024-08-21","ids":{"openalex":"https://openalex.org/W4401725938","doi":"https://doi.org/10.1109/tiv.2024.3446851"},"language":"en","primary_location":{"id":"doi:10.1109/tiv.2024.3446851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2024.3446851","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-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":null,"display_name":"Shiqi Liu","orcid":"https://orcid.org/0009-0001-7210-236X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shiqi Liu","raw_affiliation_strings":["School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-7210-236X","affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102751432","display_name":"Wenhan Cao","orcid":"https://orcid.org/0009-0001-1367-4210"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhan Cao","raw_affiliation_strings":["State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-1367-4210","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353166","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0001-7686-2510"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Liu","raw_affiliation_strings":["Department of Advanced Manufacturing and Robotics, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7686-2510","affiliations":[{"raw_affiliation_string":"Department of Advanced Manufacturing and Robotics, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tianyi Zhang","orcid":"https://orcid.org/0009-0008-2190-3889"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Zhang","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-2190-3889","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100747108","display_name":"Shengbo Eben Li","orcid":"https://orcid.org/0000-0003-4923-3633"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengbo Eben Li","raw_affiliation_strings":["School of Vehicle and Mobility and College of Artificial Intelligence, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4923-3633","affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility and College of Artificial Intelligence, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":1.3245,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83662241,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"10","issue":"5","first_page":"3113","last_page":"3123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.928600013256073,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/kalman-filter","display_name":"Kalman filter","score":0.8405348062515259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6532883048057556},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6332113742828369},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6141573786735535},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6086835861206055},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5123287439346313},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5023488998413086},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.45958036184310913},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.423200786113739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37455254793167114},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11199027299880981}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.8405348062515259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6532883048057556},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6332113742828369},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6141573786735535},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6086835861206055},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5123287439346313},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5023488998413086},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.45958036184310913},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.423200786113739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37455254793167114},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11199027299880981},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tiv.2024.3446851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2024.3446851","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2000340713","https://openalex.org/W2051992184","https://openalex.org/W2101051269","https://openalex.org/W2115579991","https://openalex.org/W2123487311","https://openalex.org/W2124781496","https://openalex.org/W2164152021","https://openalex.org/W2169103119","https://openalex.org/W2222512263","https://openalex.org/W2281196926","https://openalex.org/W2610163782","https://openalex.org/W2922728516","https://openalex.org/W2949708697","https://openalex.org/W2963251831","https://openalex.org/W3035574168","https://openalex.org/W3092149800","https://openalex.org/W3108660956","https://openalex.org/W3118212025","https://openalex.org/W3137979749","https://openalex.org/W3154571917","https://openalex.org/W3207542783","https://openalex.org/W4200051644","https://openalex.org/W4226546914","https://openalex.org/W4312894406","https://openalex.org/W4320008877","https://openalex.org/W4362650413","https://openalex.org/W4379931754","https://openalex.org/W4382203297","https://openalex.org/W4382936309","https://openalex.org/W4387385758","https://openalex.org/W4389665756","https://openalex.org/W4393317283","https://openalex.org/W4403294976"],"related_works":["https://openalex.org/W2385286892","https://openalex.org/W2350927655","https://openalex.org/W4285271403","https://openalex.org/W2542007731","https://openalex.org/W2968379562","https://openalex.org/W3156013036","https://openalex.org/W2091015105","https://openalex.org/W4388689193","https://openalex.org/W2110899030","https://openalex.org/W29633852"],"abstract_inverted_index":{"Multi-object":[0],"tracking":[1,34,48,116,157],"(MOT)":[2],"is":[3,126],"an":[4],"essential":[5],"technique":[6],"for":[7,86,146,185],"navigation":[8],"in":[9,113,159],"autonomous":[10],"driving.":[11],"In":[12],"tracking-by-detection":[13],"systems,":[14],"biases,":[15],"false":[16],"positives,":[17],"and":[18,80,174],"misses,":[19],"which":[20,164],"are":[21,26,36],"referred":[22],"to":[23,29,46,128,181],"as":[24,71],"outliers,":[25,44,129,163],"inevitable":[27],"due":[28],"complex":[30],"traffic":[31],"scenarios.":[32],"Recent":[33],"methods":[35],"based":[37],"on":[38,171],"filtering":[39],"algorithms":[40,184],"that":[41,125,137,152],"overlook":[42],"these":[43],"leading":[45],"reduced":[47],"accuracy":[49,179],"or":[50],"even":[51],"loss":[52],"of":[53,69,77,122,162],"the":[54,67,74,81,106,123,131,140,160,172],"object's":[55],"trajectory.":[56],"To":[57],"handle":[58],"this":[59,100,103],"challenge,":[60],"we":[61,97,118],"adopt":[62],"a":[63,94,120,155],"probabilistic":[64],"perspective,":[65],"regarding":[66],"generation":[68],"outliers":[70],"misspecification":[72],"between":[73],"actual":[75],"distribution":[76],"measurement":[78,83],"data":[79],"nominal":[82],"model":[84],"used":[85,108],"filtering.":[87],"We":[88,135,149],"further":[89],"demonstrate":[90],"that,":[91],"by":[92],"designing":[93],"convolutional":[95,132],"operation,":[96],"can":[98],"mitigate":[99],"misspecification.":[101],"Incorporating":[102],"operation":[104],"into":[105],"widely":[107],"unscented":[109],"Kalman":[110],"filter":[111],"(UKF)":[112],"commonly":[114],"adopted":[115],"algorithms,":[117],"derive":[119],"variant":[121],"UKF":[124,133],"robust":[127,166],"called":[130],"(ConvUKF).":[134],"show":[136,177],"ConvUKF":[138,153],"maintains":[139],"Gaussian":[141],"conjugate":[142],"property,":[143],"thus":[144],"allowing":[145],"real-time":[147],"tracking.":[148],"also":[150],"prove":[151],"has":[154],"bounded":[156],"error":[158],"presence":[161],"implies":[165],"stability.":[167],"The":[168],"experimental":[169],"results":[170],"KITTI":[173],"nuScenes":[175],"datasets":[176],"improved":[178],"compared":[180],"representative":[182],"baseline":[183],"MOT":[186],"tasks.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}