{"id":"https://openalex.org/W4297166771","doi":"https://doi.org/10.48550/arxiv.2209.11294","title":"T2FPV: Dataset and Method for Correcting First-Person View Errors in Pedestrian Trajectory Prediction","display_name":"T2FPV: Dataset and Method for Correcting First-Person View Errors in Pedestrian Trajectory Prediction","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4297166771","doi":"https://doi.org/10.48550/arxiv.2209.11294"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.11294","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2209.11294","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042951025","display_name":"Benjamin Stoler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stoler, Benjamin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044785257","display_name":"Meghdeep Jana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jana, Meghdeep","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015849702","display_name":"Soonmin Hwang","orcid":"https://orcid.org/0000-0003-1499-3253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hwang, Soonmin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5019807694","display_name":"Jean Oh","orcid":"https://orcid.org/0000-0001-9709-2658"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oh, Jean","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":60},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9989,"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":0.9989,"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":0.996,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9934,"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/imputation","display_name":"Imputation (statistics)","score":0.54154664}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.78120255},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.77497476},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.715927},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.54154664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5360402},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.48526523},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.44643673},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4188794},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.41881183},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.39542347},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36512834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35702792},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.082959175},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.11294","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2209.11294","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.11294","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.49}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4211215373","https://openalex.org/W3179858851","https://openalex.org/W3144172081","https://openalex.org/W3028371478","https://openalex.org/W2581984549","https://openalex.org/W2181530120","https://openalex.org/W2081476516","https://openalex.org/W2055961818","https://openalex.org/W2024529227","https://openalex.org/W1574575415"],"abstract_inverted_index":{"Predicting":[0],"pedestrian":[1,79],"motion":[2],"is":[3,25],"essential":[4],"for":[5,20,59],"developing":[6],"socially-aware":[7],"robots":[8],"that":[9,124],"interact":[10],"in":[11,34,42,132],"a":[12,21,57,67,122],"crowded":[13],"environment.":[14],"While":[15],"the":[16,29,43,77,83,92,113,127,143],"natural":[17],"visual":[18,85],"perspective":[19],"social":[22],"interaction":[23],"setting":[24],"an":[26,133],"egocentric":[27,84],"view,":[28],"majority":[30],"of":[31,87,112,129,145],"existing":[32],"work":[33],"trajectory":[35,45,51,70,137],"prediction":[36,52,151],"therein":[37],"has":[38],"been":[39],"investigated":[40],"purely":[41],"top-down":[44,69],"space.":[46],"To":[47,115,162],"support":[48],"first-person":[49,62],"view":[50,63],"research,":[53],"we":[54,72,119,166],"present":[55],"T2FPV,":[56],"method":[58,141],"constructing":[60],"high-fidelity":[61],"(FPV)":[64],"datasets":[65],"given":[66],"real-world,":[68],"dataset;":[71],"showcase":[73],"our":[74,168],"approach":[75],"on":[76,149,160],"ETH/UCY":[78],"dataset":[80,170],"to":[81,102],"generate":[82],"data":[86,131],"all":[88],"interacting":[89],"pedestrians,":[90],"creating":[91],"T2FPV-ETH":[93,169],"dataset.":[94],"In":[95],"this":[96],"setting,":[97],"FPV-specific":[98],"errors":[99,148],"arise":[100],"due":[101],"imperfect":[103],"detection":[104],"and":[105,108,171],"tracking,":[106],"occlusions,":[107],"field-of-view":[109],"(FOV)":[110],"limitations":[111],"camera.":[114],"address":[116],"these":[117],"errors,":[118],"propose":[120],"CoFE,":[121],"module":[123],"further":[125],"refines":[126],"imputation":[128],"missing":[130],"end-to-end":[134],"manner":[135],"with":[136],"forecasting":[138],"algorithms.":[139],"Our":[140],"reduces":[142],"impact":[144],"such":[146],"FPV":[147],"downstream":[150],"performance,":[152],"decreasing":[153],"displacement":[154],"error":[155],"by":[156],"more":[157],"than":[158],"10%":[159],"average.":[161],"facilitate":[163],"research":[164],"engagement,":[165],"release":[167],"software":[172],"tools.":[173]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4297166771","counts_by_year":[],"updated_date":"2025-01-22T17:27:56.002383","created_date":"2022-09-27"}