{"id":"https://openalex.org/W1909538523","doi":"https://doi.org/10.1109/cvpr.2015.7298873","title":"Learning to detect Motion Boundaries","display_name":"Learning to detect Motion Boundaries","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1909538523","doi":"https://doi.org/10.1109/cvpr.2015.7298873","mag":"1909538523"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://inria.hal.science/hal-01142653","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054497181","display_name":"Philippe Weinzaepfel","orcid":"https://orcid.org/0000-0002-4223-3983"},"institutions":[{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Philippe Weinzaepfel","raw_affiliation_strings":["Inria","LEAR team, Univ. Grenoble Alpes, France"],"affiliations":[{"raw_affiliation_string":"Inria","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"LEAR team, Univ. Grenoble Alpes, France","institution_ids":["https://openalex.org/I899635006"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065291945","display_name":"J\u00e9r\u00f4me Revaud","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jerome Revaud","raw_affiliation_strings":["Inria","LEAR team, Univ. Grenoble Alpes, France"],"affiliations":[{"raw_affiliation_string":"Inria","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"LEAR team, Univ. Grenoble Alpes, France","institution_ids":["https://openalex.org/I899635006"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010613767","display_name":"Za\u00efd Harchaoui","orcid":"https://orcid.org/0000-0003-1186-1343"},"institutions":[{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Zaid Harchaoui","raw_affiliation_strings":["Inria","LEAR team, Univ. Grenoble Alpes, France","NYU"],"affiliations":[{"raw_affiliation_string":"Inria","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"LEAR team, Univ. Grenoble Alpes, France","institution_ids":["https://openalex.org/I899635006"]},{"raw_affiliation_string":"NYU","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109890544","display_name":"Cordelia Schmid","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Cordelia Schmid","raw_affiliation_strings":["Inria","LEAR team, Univ. Grenoble Alpes, France"],"affiliations":[{"raw_affiliation_string":"Inria","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"LEAR team, Univ. Grenoble Alpes, France","institution_ids":["https://openalex.org/I899635006"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5054497181"],"corresponding_institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I899635006"],"apc_list":null,"apc_paid":null,"fwci":6.5466,"has_fulltext":false,"cited_by_count":57,"citation_normalized_percentile":{"value":0.97718987,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2578","last_page":"2586"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10531","display_name":"Advanced Vision and Imaging","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/T11019","display_name":"Image Enhancement Techniques","score":0.9962000250816345,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9952999949455261,"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/optical-flow","display_name":"Optical flow","score":0.8844919800758362},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7807993292808533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7317534685134888},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.6949817538261414},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.6571848392486572},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.629512369632721},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6279714703559875},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5586254000663757},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5452339053153992},{"id":"https://openalex.org/keywords/classification-of-discontinuities","display_name":"Classification of discontinuities","score":0.5355828404426575},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5138362050056458},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5035225749015808},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.42291563749313354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33433830738067627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2533755898475647},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18512949347496033},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15206405520439148}],"concepts":[{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.8844919800758362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7807993292808533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7317534685134888},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.6949817538261414},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.6571848392486572},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.629512369632721},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6279714703559875},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5586254000663757},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5452339053153992},{"id":"https://openalex.org/C15627037","wikidata":"https://www.wikidata.org/wiki/Q541961","display_name":"Classification of discontinuities","level":2,"score":0.5355828404426575},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5138362050056458},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5035225749015808},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.42291563749313354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33433830738067627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2533755898475647},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18512949347496033},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15206405520439148},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2015.7298873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.697.1311","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.697.1311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://hal.inria.fr/hal-01142653/file/cvpr15_mobo.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:hal-01142653v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-01142653","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CVPR - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2015, Boston, United States. pp.2578-2586, &#x27E8;10.1109/CVPR.2015.7298873&#x27E9;","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01142653v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-01142653","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CVPR - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2015, Boston, United States. pp.2578-2586, &#x27E8;10.1109/CVPR.2015.7298873&#x27E9;","raw_type":"Conference papers"},"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W119960974","https://openalex.org/W1513100184","https://openalex.org/W1544478103","https://openalex.org/W1578985305","https://openalex.org/W1590957170","https://openalex.org/W1597899558","https://openalex.org/W1755205674","https://openalex.org/W1867429401","https://openalex.org/W1973054923","https://openalex.org/W1973536668","https://openalex.org/W2008806336","https://openalex.org/W2012960460","https://openalex.org/W2016053056","https://openalex.org/W2035379092","https://openalex.org/W2036284314","https://openalex.org/W2068611653","https://openalex.org/W2080920426","https://openalex.org/W2110158442","https://openalex.org/W2113221323","https://openalex.org/W2113708607","https://openalex.org/W2121927366","https://openalex.org/W2129587342","https://openalex.org/W2129666410","https://openalex.org/W2131747574","https://openalex.org/W2132492105","https://openalex.org/W2142912032","https://openalex.org/W2143033699","https://openalex.org/W2144553193","https://openalex.org/W2145467074","https://openalex.org/W2146634731","https://openalex.org/W2147253850","https://openalex.org/W2155302366","https://openalex.org/W2157130620","https://openalex.org/W2165468772","https://openalex.org/W2166563909","https://openalex.org/W2167717741","https://openalex.org/W2170991533","https://openalex.org/W2308045930","https://openalex.org/W2568971933","https://openalex.org/W6604766503","https://openalex.org/W6630726011","https://openalex.org/W6639126518","https://openalex.org/W6681186194","https://openalex.org/W6681365617"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2093471820","https://openalex.org/W50079190","https://openalex.org/W2012410061","https://openalex.org/W3102673927","https://openalex.org/W2327954668","https://openalex.org/W2970427506","https://openalex.org/W2053610073","https://openalex.org/W2030154815","https://openalex.org/W2051121715"],"abstract_inverted_index":{"We":[0,103],"propose":[1],"a":[2,43,130,164],"learning-based":[3],"approach":[4,36,84],"for":[5,16],"motion":[6,12,24,40,70,150],"boundary":[7],"detection.":[8],"Precise":[9],"localization":[10],"of":[11,19,29,51,118],"boundaries":[13,25],"is":[14,85],"essential":[15],"the":[17,30,49,52,62,82,98,105,116,119,124,133],"success":[18],"optical":[20,31,111,168],"flow":[21,32,73,112,169],"estimation,":[22],"as":[23],"correspond":[26],"to":[27,38],"discontinuities":[28],"field.":[33],"The":[34,55],"proposed":[35,83],"allows":[37],"predict":[39],"boundaries,":[41],"using":[42],"structured":[44],"random":[45,56,125],"forest":[46,57],"trained":[47,158],"on":[48,97,159],"ground-truth":[50],"MPI-Sintel":[53,99],"dataset.":[54],"leverages":[58],"several":[59,109],"cues":[60,71,121],"at":[61],"patch":[63],"level,":[64],"namely":[65],"appearance":[66],"(RGB":[67],"color)":[68],"and":[69,88,100,114],"(optical":[72],"estimated":[74],"by":[75,163],"state-of-the-art":[76,94,110,167],"algorithms).":[77],"Experimental":[78],"results":[79,106],"show":[80],"that":[81,139],"both":[86],"robust":[87],"computationally":[89],"efficient.":[90],"It":[91],"significantly":[92],"outperforms":[93,162],"motion-difference":[95],"approaches":[96,113],"Middlebury":[101],"datasets.":[102],"compare":[104],"obtained":[107],"with":[108,147],"study":[115],"impact":[117],"different":[120],"used":[122],"in":[123],"forest.":[126],"Furthermore,":[127],"we":[128],"introduce":[129],"new":[131],"dataset,":[132,154],"YouTube":[134],"Motion":[135],"Boundaries":[136],"dataset":[137],"(YMB),":[138],"comprises":[140],"60":[141],"sequences":[142],"taken":[143],"from":[144],"real-world":[145],"videos":[146],"manually":[148],"annotated":[149],"boundaries.":[151],"On":[152],"this":[153],"our":[155],"approach,":[156],"although":[157],"MPI-Sintel,":[160],"also":[161],"large":[165],"margin":[166],"algorithms.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":15},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
