{"id":"https://openalex.org/W2554847032","doi":"https://doi.org/10.1109/ijcnn.2016.7727529","title":"Video anomaly detection based on ULGP-OF descriptor and one-class ELM","display_name":"Video anomaly detection based on ULGP-OF descriptor and one-class ELM","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2554847032","doi":"https://doi.org/10.1109/ijcnn.2016.7727529","mag":"2554847032"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727529","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5100420080","display_name":"Siqi Wang","orcid":"https://orcid.org/0000-0002-8134-9508"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Siqi Wang","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069681054","display_name":"En Zhu","orcid":"https://orcid.org/0000-0003-2305-7555"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"En Zhu","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089193327","display_name":"Jianping Yin","orcid":"https://orcid.org/0000-0002-5474-4764"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Yin","raw_affiliation_strings":["College of Computer, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100420080"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.4285,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.79692293,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"2","issue":null,"first_page":"2630","last_page":"2637"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9646999835968018,"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":0.8281590342521667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7278173565864563},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.716800332069397},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6534023284912109},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5602779984474182},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49112173914909363},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45888811349868774},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4136788547039032},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20166009664535522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8281590342521667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7278173565864563},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.716800332069397},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6534023284912109},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5602779984474182},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49112173914909363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45888811349868774},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4136788547039032},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20166009664535522}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727529","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320332603","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W148010982","https://openalex.org/W1411755238","https://openalex.org/W1781857629","https://openalex.org/W1957718552","https://openalex.org/W1963789929","https://openalex.org/W1982884593","https://openalex.org/W1991251598","https://openalex.org/W2012931101","https://openalex.org/W2015461918","https://openalex.org/W2019607443","https://openalex.org/W2024868105","https://openalex.org/W2026131661","https://openalex.org/W2033959528","https://openalex.org/W2042184006","https://openalex.org/W2047016883","https://openalex.org/W2051075456","https://openalex.org/W2072208715","https://openalex.org/W2074076772","https://openalex.org/W2076551186","https://openalex.org/W2110383222","https://openalex.org/W2110934250","https://openalex.org/W2111072639","https://openalex.org/W2112657736","https://openalex.org/W2116607081","https://openalex.org/W2122361470","https://openalex.org/W2126868529","https://openalex.org/W2129520225","https://openalex.org/W2138092272","https://openalex.org/W2142412278","https://openalex.org/W2153635508","https://openalex.org/W2158054309","https://openalex.org/W2163352848","https://openalex.org/W2163612318","https://openalex.org/W2163991508","https://openalex.org/W2164223054","https://openalex.org/W2164261375","https://openalex.org/W2164489414","https://openalex.org/W2164878725","https://openalex.org/W2173160820","https://openalex.org/W2399365691","https://openalex.org/W2533858083","https://openalex.org/W3120421331","https://openalex.org/W4235063816","https://openalex.org/W4247760752","https://openalex.org/W6606078247","https://openalex.org/W6662466422","https://openalex.org/W6668360100","https://openalex.org/W6677330367","https://openalex.org/W6679513582","https://openalex.org/W6680470121","https://openalex.org/W6683941694"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2012353789","https://openalex.org/W2530420969","https://openalex.org/W2051187167","https://openalex.org/W1980100242"],"abstract_inverted_index":{"Smart":[0],"video":[1,19,54,73,85,110,145,152],"analysis":[2,146],"is":[3,92],"attracting":[4],"increasing":[5],"attention":[6],"with":[7,53,130],"the":[8,39,46,60,88,95,100,201],"pervasive":[9],"use":[10],"of":[11,65,203],"surveillance":[12],"camera.":[13],"In":[14],"this":[15,158],"paper,":[16],"we":[17,43],"address":[18],"anomaly":[20,187],"detection":[21,188],"by":[22,75,105,163],"Uniform":[23],"Local":[24],"Gradient":[25],"Pattern":[26],"based":[27],"Optical":[28],"Flow":[29],"(ULGP-OF)":[30],"descriptor":[31,51],"and":[32,62,122,147,160,197],"one-class":[33,119],"extreme":[34],"learning":[35,135],"machine":[36],"(OCELM).":[37],"Using":[38],"proposed":[40],"ULGP-OF":[41,67],"descriptor,":[42],"naturally":[44],"combine":[45],"robust":[47],"2D":[48],"image":[49],"texture":[50,61],"LGP":[52],"optical":[55],"flow":[56],"to":[57,78,83,98,113,143,171],"jointly":[58],"descibe":[59],"motion":[63],"characteristics":[64],"video.":[66],"significantly":[68],"outperforms":[69],"other":[70,186],"frequently-used":[71],"classic":[72,114],"decriptors":[74],"a":[76,131],"6%":[77],"10%":[79],"EER":[80],"reduction.":[81],"As":[82],"normal":[84],"event":[86],"modeling,":[87],"newly":[89],"emergent":[90],"ELM":[91],"introduced":[93],"for":[94,149,174],"first":[96],"time":[97,103],"tackle":[99],"unbearable":[101],"training":[102,107],"incurred":[104],"massive":[106],"data":[108,115,153],"from":[109],"streams.":[111],"Compared":[112],"description":[116],"algorithms":[117],"like":[118,190],"SVM":[120],"(OCSVM)":[121],"sparse":[123,191],"coding,":[124],"OCELM":[125,175],"can":[126],"yield":[127],"competitive":[128],"results":[129],"significant":[132],"improvement":[133],"in":[134,157],"speed,":[136],"which":[137,178],"makes":[138],"our":[139,180,204],"approach":[140,181],"more":[141,183],"applicable":[142],"large-scale":[144],"easier":[148],"updating":[150],"when":[151],"are":[154],"explosively":[155],"generated":[156],"day":[159],"age.":[161],"Moreover,":[162],"adopting":[164],"consistency-based":[165],"criteria,":[166],"only":[167],"one":[168],"parameter":[169],"needs":[170],"be":[172],"appointed":[173],"before":[176],"training,":[177],"renders":[179],"much":[182],"parameter-free":[184],"than":[185],"techniques":[189],"coding.":[192],"Experiments":[193],"on":[194],"UCSD":[195],"ped1":[196],"ped2":[198],"datasets":[199],"demonstrate":[200],"effectiveness":[202],"approach.":[205]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}