{"id":"https://openalex.org/W3025441953","doi":"https://doi.org/10.1109/wacv45572.2020.9093539","title":"Estimating 3D Camera Pose from 2D Pedestrian Trajectories","display_name":"Estimating 3D Camera Pose from 2D Pedestrian Trajectories","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3025441953","doi":"https://doi.org/10.1109/wacv45572.2020.9093539","mag":"3025441953"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093539","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093539","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5100445660","display_name":"Yan Xu","orcid":"https://orcid.org/0000-0002-2636-7594"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yan Xu","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018736068","display_name":"Vivek Roy","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vivek Roy","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037322163","display_name":"Kris Kitani","orcid":"https://orcid.org/0000-0002-9389-4060"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kris Kitani","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100445660"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":2.071,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87532394,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2568","last_page":"2577"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9983000159263611,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7981175184249878},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7763498425483704},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7597169876098633},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7386527061462402},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.7305602431297302},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5697883367538452},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.47567954659461975},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.4733441174030304},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.44294169545173645},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.42020153999328613},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12277626991271973},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0996546745300293}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7981175184249878},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763498425483704},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7597169876098633},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7386527061462402},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7305602431297302},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5697883367538452},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.47567954659461975},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.4733441174030304},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.44294169545173645},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.42020153999328613},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12277626991271973},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0996546745300293},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093539","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093539","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1594048773","https://openalex.org/W1903029394","https://openalex.org/W1971271192","https://openalex.org/W1985304392","https://openalex.org/W1991544872","https://openalex.org/W2028730691","https://openalex.org/W2053842193","https://openalex.org/W2085261163","https://openalex.org/W2090062256","https://openalex.org/W2111151479","https://openalex.org/W2118964384","https://openalex.org/W2122892682","https://openalex.org/W2129597587","https://openalex.org/W2131774270","https://openalex.org/W2134475857","https://openalex.org/W2142482451","https://openalex.org/W2147240366","https://openalex.org/W2157929926","https://openalex.org/W2163155500","https://openalex.org/W2181274189","https://openalex.org/W2200124539","https://openalex.org/W2346442557","https://openalex.org/W2398802088","https://openalex.org/W2470404996","https://openalex.org/W2511791013","https://openalex.org/W2548409953","https://openalex.org/W2556455135","https://openalex.org/W2584731199","https://openalex.org/W2602709638","https://openalex.org/W2605111497","https://openalex.org/W2609786623","https://openalex.org/W2737630486","https://openalex.org/W2749379418","https://openalex.org/W2771385090","https://openalex.org/W2780675120","https://openalex.org/W2795645133","https://openalex.org/W2798302276","https://openalex.org/W2892865870","https://openalex.org/W2899771611","https://openalex.org/W2904086477","https://openalex.org/W2959141096","https://openalex.org/W2963210849","https://openalex.org/W2963523575","https://openalex.org/W2963575844","https://openalex.org/W2963856988","https://openalex.org/W2964121744","https://openalex.org/W2964175348","https://openalex.org/W2967775316","https://openalex.org/W2969313387","https://openalex.org/W3099342433","https://openalex.org/W6631190155","https://openalex.org/W6646686814","https://openalex.org/W6679422537","https://openalex.org/W6683122060","https://openalex.org/W6684181579","https://openalex.org/W6712210154","https://openalex.org/W6725558437","https://openalex.org/W6756040250","https://openalex.org/W6757310020","https://openalex.org/W6765982053"],"related_works":["https://openalex.org/W4253893311","https://openalex.org/W2798721181","https://openalex.org/W3201205132","https://openalex.org/W4287600488","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4281696776","https://openalex.org/W4318148659","https://openalex.org/W4387967917","https://openalex.org/W4299867837"],"abstract_inverted_index":{"We":[0,66,84,137,163],"consider":[1],"the":[2,6,130,156,172],"task":[3],"of":[4,9,184],"re-calibrating":[5],"3D":[7,47,52,73,93,134],"pose":[8,15,32,79],"a":[10,38,109,114,121],"static":[11],"surveillance":[12],"camera,":[13],"whose":[14],"may":[16],"change":[17],"due":[18],"to":[19,61,75,119,133,151,158,196],"external":[20],"forces,":[21],"such":[22],"as":[23],"birds,":[24],"wind,":[25],"falling":[26],"objects":[27],"or":[28,59],"earthquakes.":[29],"Conventionally,":[30],"camera":[31,78,101,135],"estimation":[33],"can":[34,96,147],"be":[35,97,148],"solved":[36],"with":[37],"PnP":[39],"(Perspective-n-Point)":[40],"method":[41,166,180],"using":[42,81],"2D-to-3D":[43],"feature":[44],"correspondences,":[45],"when":[46,194],"points":[48],"are":[49,55],"known.":[50],"However,":[51],"point":[53],"annotations":[54],"not":[56],"always":[57],"available":[58],"practical":[60],"obtain":[62],"in":[63],"real-world":[64],"applications.":[65],"propose":[67,108],"an":[68,182],"alternative":[69],"strategy":[70],"for":[71,77,99],"extracting":[72],"information":[74,94],"solve":[76],"by":[80,112],"pedestrian":[82,88,126],"trajectories.":[83],"observe":[85],"that":[86,95,139],"2D":[87,125],"trajectories":[89,127],"indirectly":[90],"contain":[91],"useful":[92],"used":[98],"inferring":[100],"pose.":[102,136],"To":[103],"leverage":[104],"this":[105],"information,":[106],"we":[107],"data-driven":[110],"approach":[111],"training":[113],"neural":[115],"network":[116],"(NN)":[117],"regressor":[118,141],"model":[120],"direct":[122],"mapping":[123],"from":[124,171],"projected":[128],"on":[129,144,187],"image":[131],"plane":[132],"demonstrate":[138],"our":[140,165],"trained":[142],"only":[143],"synthetic":[145],"data":[146],"directly":[149],"applied":[150],"real":[152,161],"data,":[153],"thus":[154],"eliminating":[155],"need":[157],"label":[159],"any":[160],"data.":[162],"evaluate":[164],"across":[167],"six":[168],"different":[169],"scenes":[170],"Town":[173],"Centre":[174],"Street":[175],"and":[176,190],"DUKEMTMC":[177],"datasets.":[178],"Our":[179],"achieves":[181],"improvement":[183],"~":[185],"50%":[186],"both":[188],"position":[189],"orientation":[191],"prediction":[192],"accuracy":[193],"compared":[195],"other":[197],"SOTA":[198],"methods.":[199]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
