{"id":"https://openalex.org/W7138076375","doi":"https://doi.org/10.1609/aaai.v40i15.38242","title":"Delving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking","display_name":"Delving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138076375","doi":"https://doi.org/10.1609/aaai.v40i15.38242"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i15.38242","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i15.38242","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38242/42204","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38242/42204","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129643546","display_name":"Haonan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haonan Zhang","raw_affiliation_strings":["Zhejiang University\nFabu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University\nFabu","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111140777","display_name":"X. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyao Wang","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129724839","display_name":"Boxi Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210161972","display_name":"Technological University Dawei","ror":"https://ror.org/009t1k130","country_code":"MM","type":"education","lineage":["https://openalex.org/I4210161972"]}],"countries":["MM"],"is_corresponding":false,"raw_author_name":"Boxi Wu","raw_affiliation_strings":["Zhejiang University\nDaerwen AI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University\nDaerwen AI","institution_ids":["https://openalex.org/I4210161972"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129732947","display_name":"Tu Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089508","display_name":"Feminist Archive North","ror":"https://ror.org/009rcpx96","country_code":"GB","type":"archive","lineage":["https://openalex.org/I4210089508"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tu Zheng","raw_affiliation_strings":["Fabu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fabu","institution_ids":["https://openalex.org/I4210089508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129671700","display_name":"Wang Yunhua","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111298","display_name":"Logistics Management Institute (United States)","ror":"https://ror.org/01xn7cv95","country_code":"US","type":"company","lineage":["https://openalex.org/I4210111298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wang Yunhua","raw_affiliation_strings":["Shandong Port Land-Sea International Logistics Group Co., Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Port Land-Sea International Logistics Group Co., Ltd","institution_ids":["https://openalex.org/I4210111298"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129663797","display_name":"Zheng Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210089508","display_name":"Feminist Archive North","ror":"https://ror.org/009rcpx96","country_code":"GB","type":"archive","lineage":["https://openalex.org/I4210089508"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zheng Yang","raw_affiliation_strings":["Fabu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fabu","institution_ids":["https://openalex.org/I4210089508"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"15","first_page":"12484","last_page":"12492"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8288999795913696,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8288999795913696,"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":0.09830000251531601,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.006800000090152025,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6675000190734863},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6074000000953674},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5849000215530396},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.583899974822998},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4909999966621399},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.40950000286102295},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3813000023365021},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.3725999891757965},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3700000047683716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7318000197410583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6686999797821045},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6675000190734863},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6074000000953674},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5849000215530396},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.583899974822998},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5551000237464905},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.40950000286102295},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C2987395694","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Feature tracking","level":3,"score":0.33869999647140503},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3271999955177307},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3228999972343445},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2786000072956085},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27630001306533813},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i15.38242","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i15.38242","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38242/42204","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/38242","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/38242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i15.38242","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i15.38242","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38242/42204","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7424259185791016,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1488486387","display_name":null,"funder_award_id":"62273302","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138076375.pdf","grobid_xml":"https://content.openalex.org/works/W7138076375.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"3D":[0],"multi-object":[1],"tracking":[2],"is":[3],"a":[4,113,147],"critical":[5],"and":[6,79,91,143,164,171,189,195],"challenging":[7],"task":[8],"in":[9,32,39],"the":[10,49,57,100,162,169,177,193],"field":[11],"of":[12,173],"autonomous":[13],"driving.":[14],"A":[15],"common":[16],"paradigm":[17],"relies":[18],"on":[19,88,161,192],"modeling":[20],"individual":[21],"object":[22],"motion,":[23],"e.g.,":[24],"Kalman":[25],"filters,":[26],"to":[27,59,70,76,106,122],"predict":[28],"trajectories.":[29],"While":[30],"effective":[31],"simple":[33],"scenarios,":[34],"this":[35,108],"approach":[36],"often":[37],"struggles":[38],"crowded":[40],"environments":[41],"or":[42],"with":[43],"inaccurate":[44],"detections,":[45],"as":[46],"it":[47],"overlooks":[48],"rich":[50],"geometric":[51],"relationships":[52],"between":[53,140],"objects.":[54],"This":[55],"highlights":[56],"need":[58],"leverage":[60],"spatial":[61,94],"cues.":[62],"However,":[63],"existing":[64],"geometry-aware":[65],"methods":[66],"can":[67],"be":[68],"susceptible":[69],"interference":[71],"from":[72],"irrelevant":[73],"objects,":[74],"leading":[75],"ambiguous":[77],"features":[78],"incorrect":[80],"associations.":[81],"To":[82],"address":[83],"this,":[84],"we":[85,111],"propose":[86],"focusing":[87],"cue-consistency:":[89],"identifying":[90],"matching":[92],"stable":[93,156],"patterns":[95],"over":[96],"time.":[97],"We":[98],"introduce":[99],"Dynamic":[101],"Scene":[102],"Cue-Consistency":[103],"Tracker":[104],"(DSC-Track)":[105],"implement":[107],"principle.":[109],"Firstly,":[110],"design":[112],"unified":[114],"spatiotemporal":[115,153],"encoder":[116],"using":[117],"Point":[118],"Pair":[119],"Features":[120],"(PPF)":[121],"learn":[123],"discriminative":[124],"trajectory":[125],"embeddings":[126],"while":[127],"suppressing":[128],"interference.":[129],"Secondly,":[130],"our":[131,174,182],"cue-consistency":[132],"transformer":[133],"module":[134],"explicitly":[135],"aligns":[136],"consistent":[137],"feature":[138],"representations":[139],"historical":[141],"tracks":[142],"current":[144],"detections.":[145],"Finally,":[146],"dynamic":[148],"update":[149],"mechanism":[150],"preserves":[151],"salient":[152],"information":[154],"for":[155,180],"online":[157],"tracking.":[158],"Extensive":[159],"experiments":[160],"nuScenes":[163,178],"Waymo":[165],"Open":[166],"Datasets":[167],"validate":[168],"effectiveness":[170],"robustness":[172],"approach.":[175],"On":[176],"benchmark,":[179],"instance,":[181],"method":[183],"achieves":[184],"state-of-the-art":[185],"performance,":[186],"reaching":[187],"73.2%":[188],"70.3%":[190],"AMOTA":[191],"validation":[194],"test":[196],"sets,":[197],"respectively.":[198]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}