{"id":"https://openalex.org/W2010783580","doi":"https://doi.org/10.1145/2502081.2502155","title":"Spatio-temporal fisher vector coding for surveillance event detection","display_name":"Spatio-temporal fisher vector coding for surveillance event detection","publication_year":2013,"publication_date":"2013-10-21","ids":{"openalex":"https://openalex.org/W2010783580","doi":"https://doi.org/10.1145/2502081.2502155","mag":"2010783580"},"language":"en","primary_location":{"id":"doi:10.1145/2502081.2502155","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2502081.2502155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international conference on Multimedia","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/A5100435670","display_name":"Qiang Chen","orcid":"https://orcid.org/0000-0002-2825-5393"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Qiang Chen","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102877899","display_name":"Yang Cai","orcid":"https://orcid.org/0000-0002-5426-1324"},"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":"Yang Cai","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087565623","display_name":"Lisa M. Brown","orcid":"https://orcid.org/0000-0002-3793-7310"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lisa Brown","raw_affiliation_strings":["IBM Research, New York, USA","IBM research, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, New York, USA","institution_ids":[]},{"raw_affiliation_string":"IBM research, New York, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110243669","display_name":"Ankur Datta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ankur Datta","raw_affiliation_strings":["IBM Research, New York, USA","IBM research, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, New York, USA","institution_ids":[]},{"raw_affiliation_string":"IBM research, New York, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114069812","display_name":"Quanfu Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quanfu Fan","raw_affiliation_strings":["IBM Research, New York, USA","IBM research, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, New York, USA","institution_ids":[]},{"raw_affiliation_string":"IBM research, New York, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111930150","display_name":"Rog\u00e9rio Feris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rogerio Feris","raw_affiliation_strings":["IBM Research, New York, USA","IBM research, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, New York, USA","institution_ids":[]},{"raw_affiliation_string":"IBM research, New York, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381753","display_name":"Shuicheng Yan","orcid":"https://orcid.org/0000-0001-8906-3777"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shuicheng Yan","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113665305","display_name":"Alex Hauptmann","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":"Alex Hauptmann","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078542580","display_name":"Sharath Pankanti","orcid":"https://orcid.org/0000-0001-6770-9899"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharath Pankanti","raw_affiliation_strings":["IBM Research, New York, USA","IBM research, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, New York, USA","institution_ids":[]},{"raw_affiliation_string":"IBM research, New York, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5100435670"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":2.4875,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.9044466,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"589","last_page":"592"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9965000152587891,"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/computer-science","display_name":"Computer science","score":0.7180296182632446},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.6898980140686035},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6884778141975403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6410061717033386},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6046643257141113},{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.562738835811615},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5507996678352356},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5344471335411072},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5185806751251221},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5084244608879089},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5066633820533752},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4325549006462097},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42165929079055786},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32438963651657104},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.30735519528388977},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.2061951458454132},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1425224244594574}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7180296182632446},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.6898980140686035},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6884778141975403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6410061717033386},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6046643257141113},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.562738835811615},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5507996678352356},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5344471335411072},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5185806751251221},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5084244608879089},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5066633820533752},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4325549006462097},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42165929079055786},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32438963651657104},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.30735519528388977},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2061951458454132},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1425224244594574},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"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/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2502081.2502155","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2502081.2502155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21st ACM international conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:scholarbank.nus.edu.sg:10635/84210","is_oa":false,"landing_page_url":"http://scholarbank.nus.edu.sg/handle/10635/84210","pdf_url":null,"source":{"id":"https://openalex.org/S7407052290","display_name":"National University of Singapore","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W1534763723","https://openalex.org/W1540155273","https://openalex.org/W1551774182","https://openalex.org/W1604621531","https://openalex.org/W1606858007","https://openalex.org/W1976921161","https://openalex.org/W2027922120","https://openalex.org/W2031489346","https://openalex.org/W2034328688","https://openalex.org/W2062903088","https://openalex.org/W2097018403","https://openalex.org/W2103140817","https://openalex.org/W2107034620","https://openalex.org/W2108598243","https://openalex.org/W2119799051","https://openalex.org/W2139212933","https://openalex.org/W2162915993","https://openalex.org/W2165966284","https://openalex.org/W3193477162"],"related_works":["https://openalex.org/W2623931658","https://openalex.org/W2156150980","https://openalex.org/W2165047624","https://openalex.org/W2107628111","https://openalex.org/W2136112386","https://openalex.org/W1788497923","https://openalex.org/W2394004323","https://openalex.org/W1608857994","https://openalex.org/W2615441832","https://openalex.org/W2010783580"],"abstract_inverted_index":{"We":[0,18,195,212],"present":[1],"a":[2,20,91,136,189,206],"generic":[3],"event":[4,29,112,226,253],"detection":[5,230],"system":[6,102],"evaluated":[7],"in":[8,121,157,161,244],"the":[9,35,62,65,74,83,104,130,162,175,180,224,237,251],"Surveillance":[10],"Event":[11],"Detection":[12],"(SED)":[13],"task":[14],"of":[15,64,100,106,149,159,216,239,250],"TRECVID":[16,245],"2012.":[17],"investigate":[19],"statistical":[21],"approach":[22,39,235],"with":[23],"spatio-temporal":[24,44],"features":[25,134,183],"applied":[26],"to":[27,60,81,128,141,221],"seven":[28,252],"classes,":[30],"which":[31,203],"were":[32],"defined":[33],"by":[34,50],"SED":[36,246],"task.":[37],"This":[38,234],"is":[40,58,79,88,103,127],"based":[41],"on":[42,248],"local":[43,182],"descriptors,":[45],"called":[46],"MoSIFT":[47],"and":[48,140,184,231],"generated":[49],"pair-wise":[51],"video":[52,111,176,225],"frames.":[53],"A":[54],"Gaussian":[55,137],"Mixture":[56,138],"Model(GMM)":[57],"learned":[59],"model":[61,87,129,191],"distribution":[63],"low":[66,131],"level":[67,132],"features.":[68,150],"Then":[69],"for":[70,94,147,192,200],"each":[71,95],"sliding":[72],"window,":[73],"Fisher":[75,107,114],"vector":[76,108,115,145],"encoding":[77,109,116,152,202],"[improvedFV]":[78],"used":[80],"generate":[82,142],"sample":[84],"representation.":[85,194],"The":[86,97,124],"learnt":[89],"using":[90],"Linear":[92],"SVM":[93],"event.":[96],"main":[98],"novelty":[99],"our":[101],"introduction":[105],"into":[110,223],"detection.":[113],"has":[117,166],"demonstrated":[118],"great":[119],"success":[120],"image":[122],"classification.":[123],"key":[125],"idea":[126],"visual":[133],"as":[135],"Model":[139],"an":[143,197],"intermediate":[144],"representation":[146],"bag":[148],"FV":[151,165,201],"uses":[153],"higher":[154],"order":[155],"statistics":[156],"place":[158],"histograms":[160],"standard":[163],"BoW.":[164],"several":[167],"good":[168],"properties:":[169],"(a)":[170],"it":[171],"can":[172,187,204],"naturally":[173],"separate":[174],"specific":[177],"information":[178],"from":[179],"noisy":[181],"(b)":[185],"we":[186],"use":[188],"linear":[190],"this":[193],"build":[196],"efficient":[198],"implementation":[199],"attain":[205],"10":[207],"times":[208],"speed-up":[209],"over":[210],"real-time.":[211],"also":[213],"take":[214],"advantage":[215],"non-trivial":[217],"object":[218],"localization":[219],"techniques":[220],"feed":[222],"detection,":[227],"e.g.":[228],"multi-scale":[229],"non-maximum":[232],"suppression.":[233],"outperformed":[236],"results":[238],"all":[240],"other":[241],"teams":[242],"submissions":[243],"2012":[247],"four":[249],"types.":[254]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":4},{"year":2014,"cited_by_count":5}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
