{"id":"https://openalex.org/W4416241114","doi":"https://doi.org/10.1109/iccv51701.2025.02363","title":"ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting","display_name":"ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416241114","doi":"https://doi.org/10.1109/iccv51701.2025.02363"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.02363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.02363","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.07089","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091957449","display_name":"Sandro Papais","orcid":null},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Sandro Papais","raw_affiliation_strings":["University of Toronto"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045596261","display_name":"Letian Wang","orcid":"https://orcid.org/0000-0002-0231-6960"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Letian Wang","raw_affiliation_strings":["University of Toronto"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113284709","display_name":"Brian Cheong","orcid":"https://orcid.org/0009-0000-4110-6504"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Brian Cheong","raw_affiliation_strings":["University of Toronto"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024242059","display_name":"Steven L. Waslander","orcid":"https://orcid.org/0000-0003-4217-4415"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Steven L. Waslander","raw_affiliation_strings":["University of Toronto"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5091957449"],"corresponding_institution_ids":["https://openalex.org/I185261750"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34419242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"25474","last_page":"25484"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.39809998869895935,"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":0.39809998869895935,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.32589998841285706,"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":0.13660000264644623,"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/leverage","display_name":"Leverage (statistics)","score":0.6189000010490417},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5407999753952026},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4375999867916107},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.42179998755455017},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4000999927520752},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.350600004196167},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.34880000352859497},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.3028999865055084}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6916000247001648},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6189000010490417},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5407999753952026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5004000067710876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4595000147819519},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.413100004196167},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4000999927520752},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2874000072479248},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C64848388","wikidata":"https://www.wikidata.org/wiki/Q188867","display_name":"Futures studies","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.02363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.02363","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.07089","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.07089","pdf_url":"https://arxiv.org/pdf/2508.07089","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.07089","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.07089","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.07089","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.07089","pdf_url":"https://arxiv.org/pdf/2508.07089","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"ForeSight,":[2],"a":[3,39,70,105],"novel":[4],"joint":[5],"detection":[6,20,47,60,145],"and":[7,21,42,48,54,87,141,146],"forecasting":[8,22,49,147],"framework":[9],"for":[10,97],"vision-based":[11],"3D":[12],"perception":[13],"in":[14],"autonomous":[15],"vehicles.":[16],"Traditional":[17],"approaches":[18],"treat":[19],"as":[23],"separate":[24],"sequential":[25],"tasks,":[26],"limiting":[27],"their":[28],"ability":[29],"to":[30,50],"leverage":[31],"temporal":[32,82],"cues.":[33],"ForeSight":[34,93,121],"addresses":[35],"this":[36],"limitation":[37],"with":[38,104],"multi-task":[40],"streaming":[41,78],"bidirectional":[43],"learning":[44],"approach,":[45],"allowing":[46],"share":[51],"query":[52],"memory":[53,74],"propagate":[55],"information":[56],"seamlessly.":[57],"The":[58],"forecast-aware":[59],"transformer":[61,80],"enhances":[62],"spatial":[63],"reasoning":[64],"by":[65,133],"integrating":[66],"trajectory":[67],"predictions":[68],"from":[69],"multiple":[71],"hypothesis":[72],"forecast":[73,79],"queue,":[75],"while":[76,135],"the":[77,95,116,138],"improves":[81],"consistency":[83],"using":[84],"past":[85],"forecasts":[86],"refined":[88],"detections.":[89],"Unlike":[90],"tracking-based":[91],"methods,":[92],"eliminates":[94],"need":[96],"explicit":[98],"object":[99],"association,":[100],"reducing":[101],"error":[102],"propagation":[103],"tracking-free":[106],"model":[107],"that":[108,120],"efficiently":[109],"scales":[110],"across":[111],"multi-frame":[112],"sequences.":[113],"Experiments":[114],"on":[115],"nuScenes":[117],"dataset":[118],"show":[119],"achieves":[122],"state-of-the-art":[123],"performance,":[124],"achieving":[125],"an":[126],"EPA":[127],"of":[128],"54.9%,":[129],"surpassing":[130],"previous":[131],"methods":[132],"9.3%,":[134],"also":[136],"attaining":[137],"best":[139],"mAP":[140],"minADE":[142],"among":[143],"multi-view":[144],"models.":[148]},"counts_by_year":[],"updated_date":"2026-05-06T06:03:25.996018","created_date":"2025-10-10T00:00:00"}
