{"id":"https://openalex.org/W1969236304","doi":"https://doi.org/10.1109/cvpr.2010.5540152","title":"Efficient extraction of human motion volumes by tracking","display_name":"Efficient extraction of human motion volumes by tracking","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W1969236304","doi":"https://doi.org/10.1109/cvpr.2010.5540152","mag":"1969236304"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5540152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5540152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","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/A5018518655","display_name":"Juan Carlos Niebles","orcid":"https://orcid.org/0000-0001-8225-9793"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]},{"id":"https://openalex.org/I142879360","display_name":"Universidad del Norte","ror":"https://ror.org/031e6xm45","country_code":"CO","type":"education","lineage":["https://openalex.org/I142879360"]}],"countries":["CO","US"],"is_corresponding":true,"raw_author_name":"Juan Carlos Niebles","raw_affiliation_strings":["Princeton University, USA","Universidad del Norte, Colombia"],"affiliations":[{"raw_affiliation_string":"Princeton University, USA","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Universidad del Norte, Colombia","institution_ids":["https://openalex.org/I142879360"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006594639","display_name":"Bohyung Han","orcid":"https://orcid.org/0000-0003-3099-3616"},"institutions":[{"id":"https://openalex.org/I48566637","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748","country_code":"KR","type":"education","lineage":["https://openalex.org/I48566637"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bohyung Han","raw_affiliation_strings":["Electrical and Computer Engineering, UNIST, South Korea"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, UNIST, South Korea","institution_ids":["https://openalex.org/I48566637"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100450462","display_name":"Li Fei-Fei","orcid":"https://orcid.org/0000-0002-7481-0810"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Fei-Fei","raw_affiliation_strings":["Computer Science Department, University of Stanford, USA"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, University of Stanford, USA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018518655"],"corresponding_institution_ids":["https://openalex.org/I142879360","https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":5.1724,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95829328,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"i","issue":null,"first_page":"655","last_page":"662"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9991000294685364,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/computer-science","display_name":"Computer science","score":0.7942361831665039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6549119353294373},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5910878777503967},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5613731145858765},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5583443641662598},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5246762633323669},{"id":"https://openalex.org/keywords/top-down-and-bottom-up-design","display_name":"Top-down and bottom-up design","score":0.5206743478775024},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4837334454059601},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.47774046659469604},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.44843852519989014},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44332292675971985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4339498281478882},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.42183300852775574},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21235385537147522},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.13571149110794067}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7942361831665039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6549119353294373},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5910878777503967},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5613731145858765},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5583443641662598},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5246762633323669},{"id":"https://openalex.org/C135798126","wikidata":"https://www.wikidata.org/wiki/Q2167279","display_name":"Top-down and bottom-up design","level":2,"score":0.5206743478775024},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4837334454059601},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.47774046659469604},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.44843852519989014},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44332292675971985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4339498281478882},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.42183300852775574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21235385537147522},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.13571149110794067},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","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/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2010.5540152","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5540152","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.172.5796","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.5796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.umd.edu/%7Ebhhan/papers/cvpr2010.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308943","display_name":"Microsoft Research","ror":"https://ror.org/00d0nc645"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1510601258","https://openalex.org/W1551898955","https://openalex.org/W1593040460","https://openalex.org/W2022699039","https://openalex.org/W2030536784","https://openalex.org/W2035379092","https://openalex.org/W2052065843","https://openalex.org/W2075386676","https://openalex.org/W2097956967","https://openalex.org/W2099913338","https://openalex.org/W2103846358","https://openalex.org/W2110379134","https://openalex.org/W2112607495","https://openalex.org/W2121742479","https://openalex.org/W2122184585","https://openalex.org/W2131432112","https://openalex.org/W2131768110","https://openalex.org/W2136606146","https://openalex.org/W2139461634","https://openalex.org/W2141218264","https://openalex.org/W2144124341","https://openalex.org/W2155343350","https://openalex.org/W2158638750","https://openalex.org/W2160142792","https://openalex.org/W3148215922","https://openalex.org/W4235246534","https://openalex.org/W6630493829","https://openalex.org/W6633008415","https://openalex.org/W6635689683","https://openalex.org/W6675153020","https://openalex.org/W6677607857","https://openalex.org/W6680374889"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W2063076820"],"abstract_inverted_index":{"We":[0,103],"present":[1],"an":[2,69],"automatic":[3],"and":[4,18,40,97,116],"efficient":[5,70],"method":[6,109],"to":[7,32,110],"extract":[8,111],"spatio-temporal":[9],"human":[10,46,90,101,112],"volumes":[11],"from":[12,118,124],"video,":[13],"which":[14,49,73],"combines":[15],"top-down":[16,24,79],"model-based":[17],"bottom-up":[19,55,66],"appearance-based":[20],"approaches.":[21],"From":[22],"the":[23,45,60,77,88,105],"perspective,":[25],"our":[26,108],"algorithm":[27],"applies":[28],"shape":[29],"priors":[30],"probabilistically":[31],"candidate":[33],"image":[34],"regions":[35,114],"obtained":[36],"by":[37],"pedestrian":[38],"detection,":[39],"provides":[41,98],"accurate":[42],"estimates":[43],"of":[44,59,76,107],"body":[47,113],"areas":[48],"serve":[50],"as":[51],"important":[52],"constraints":[53],"for":[54],"processing.":[56],"Temporal":[57],"propagation":[58,96],"identified":[61],"region":[62],"is":[63,82],"performed":[64],"with":[65],"cues":[67],"in":[68],"level-set":[71],"framework,":[72],"takes":[74],"advantage":[75],"sparse":[78],"information":[80],"that":[81],"available.":[83],"Our":[84],"formulation":[85],"also":[86],"optimizes":[87],"extracted":[89],"volume":[91],"across":[92],"frames":[93],"through":[94],"belief":[95],"temporally":[99],"coherent":[100],"regions.":[102],"demonstrate":[104],"ability":[106],"efficiently":[115],"automatically":[117],"a":[119],"large,":[120],"challenging":[121],"dataset":[122],"collected":[123],"YouTube.":[125]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}