{"id":"https://openalex.org/W2052509696","doi":"https://doi.org/10.1145/2668956.2668962","title":"Polynormal Fisher vector for activity recognition from depth sequences","display_name":"Polynormal Fisher vector for activity recognition from depth sequences","publication_year":2014,"publication_date":"2014-11-17","ids":{"openalex":"https://openalex.org/W2052509696","doi":"https://doi.org/10.1145/2668956.2668962","mag":"2052509696"},"language":"en","primary_location":{"id":"doi:10.1145/2668956.2668962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2668956.2668962","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIGGRAPH Asia 2014 Autonomous Virtual Humans and Social Robot for Telepresence","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/A5100755421","display_name":"Xiaodong Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaodong Yang","raw_affiliation_strings":["City University of New York"],"affiliations":[{"raw_affiliation_string":"City University of New York","institution_ids":["https://openalex.org/I174216632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074244244","display_name":"Yingli Tian","orcid":"https://orcid.org/0000-0003-4458-360X"},"institutions":[{"id":"https://openalex.org/I174216632","display_name":"City University of New York","ror":"https://ror.org/00453a208","country_code":"US","type":"education","lineage":["https://openalex.org/I174216632"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"YingLi Tian","raw_affiliation_strings":["City University of New York","CITY UNIVERSITY OF NEW YORK"],"affiliations":[{"raw_affiliation_string":"City University of New York","institution_ids":["https://openalex.org/I174216632"]},{"raw_affiliation_string":"CITY UNIVERSITY OF NEW YORK","institution_ids":["https://openalex.org/I174216632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100755421"],"corresponding_institution_ids":["https://openalex.org/I174216632"],"apc_list":null,"apc_paid":null,"fwci":0.4943,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.69937724,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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":0.9979000091552734,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9958000183105469,"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":0.6964656114578247},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.645700216293335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6308561563491821},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6177374124526978},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5946165323257446},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5892972946166992},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5756814479827881},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5722545385360718},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5013484954833984},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4976058304309845},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4638189673423767},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26065748929977417},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08670505881309509},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07915052771568298}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6964656114578247},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.645700216293335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6308561563491821},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6177374124526978},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5946165323257446},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5892972946166992},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5756814479827881},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5722545385360718},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5013484954833984},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4976058304309845},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4638189673423767},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26065748929977417},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08670505881309509},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07915052771568298},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/2668956.2668962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2668956.2668962","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIGGRAPH Asia 2014 Autonomous Virtual Humans and Social Robot for Telepresence","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.670.6671","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.670.6671","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://media-lab.engr.ccny.cuny.edu/%7Emedia-server/Publications/SG-AVH-2014.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.686.2293","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.686.2293","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://yangxd.org/publications/papers/PFV.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1568156700","https://openalex.org/W1591677984","https://openalex.org/W1606858007","https://openalex.org/W1966385142","https://openalex.org/W2008824967","https://openalex.org/W2016387968","https://openalex.org/W2031334527","https://openalex.org/W2060280062","https://openalex.org/W2085735683","https://openalex.org/W2090042335","https://openalex.org/W2091911422","https://openalex.org/W2118585731","https://openalex.org/W2139857301","https://openalex.org/W2143267104","https://openalex.org/W2144380653","https://openalex.org/W2162415752","https://openalex.org/W2169251375","https://openalex.org/W2172156083","https://openalex.org/W2173046955","https://openalex.org/W6677656871"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175"],"abstract_inverted_index":{"The":[0],"advent":[1],"of":[2,9,96,111],"depth":[3,34,44,91,113],"sensors":[4],"has":[5],"facilitated":[6],"a":[7,20,33,43,85,90,94,112],"variety":[8],"visual":[10],"recognition":[11],"tasks":[12],"including":[13],"human":[14,26],"activity":[15],"understanding.":[16],"This":[17],"paper":[18],"presents":[19],"novel":[21],"feature":[22],"representation":[23,110],"to":[24,46,62,74,88],"recognize":[25],"activities":[27],"from":[28,42,102],"video":[29],"sequences":[30],"captured":[31],"by":[32],"camera.":[35],"We":[36],"assemble":[37],"local":[38,53],"neighboring":[39],"hypersurface":[40],"normals":[41],"sequence":[45,92],"form":[47],"the":[48,64,68,76,108,122,126],"polynormal":[49],"which":[50],"jointly":[51],"encodes":[52],"motion":[54],"and":[55,80,133],"shape":[56],"cues.":[57],"Fisher":[58,70,100],"vector":[59],"is":[60],"employed":[61],"aggregate":[63],"low-level":[65],"polynormals":[66],"into":[67,93],"Polynormal":[69,99],"Vector.":[71],"In":[72],"order":[73],"capture":[75],"global":[77],"spatial":[78],"layout":[79],"temporal":[81],"order,":[82],"we":[83],"employ":[84],"spatio-temporal":[86],"pyramid":[87],"subdivide":[89],"set":[95],"space-time":[97],"cells.":[98],"Vectors":[101],"these":[103],"cells":[104],"are":[105],"combined":[106],"as":[107],"final":[109],"video.":[114],"Experimental":[115],"results":[116,124],"demonstrate":[117],"that":[118],"our":[119],"method":[120],"achieves":[121],"state-of-the-art":[123],"on":[125],"two":[127],"public":[128],"benchmark":[129],"datasets,":[130],"i.e.,":[131],"MSRAction3D":[132],"MSRGesture3D.":[134]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}