{"id":"https://openalex.org/W2057926892","doi":"https://doi.org/10.1109/icmlc.2014.7009142","title":"Abnormal crowd event detection based on outlier in time series","display_name":"Abnormal crowd event detection based on outlier in time series","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2057926892","doi":"https://doi.org/10.1109/icmlc.2014.7009142","mag":"2057926892"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2014.7009142","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2014.7009142","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Conference on Machine Learning and Cybernetics","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/A5108459805","display_name":"Wei-Lieh Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I50519452","display_name":"Lunghwa University of Science and Technology","ror":"https://ror.org/001y2wd07","country_code":"TW","type":"education","lineage":["https://openalex.org/I50519452"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wei-Lieh Hsu","raw_affiliation_strings":["Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan County, Taiwan","Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan County 33306, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan County, Taiwan","institution_ids":["https://openalex.org/I50519452"]},{"raw_affiliation_string":"Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan County 33306, Taiwan","institution_ids":["https://openalex.org/I50519452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410460","display_name":"Yucheng Wang","orcid":"https://orcid.org/0000-0002-6185-1680"},"institutions":[{"id":"https://openalex.org/I65196183","display_name":"Tatung University","ror":"https://ror.org/030m18266","country_code":"TW","type":"education","lineage":["https://openalex.org/I65196183"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Cheng Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, Tatung University, Taipei, Taiwan","Dept. of Comput. Sci. & Eng., Tatung Univ., Taipei, Taiwan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Tatung University, Taipei, Taiwan","institution_ids":["https://openalex.org/I65196183"]},{"raw_affiliation_string":"Dept. of Comput. Sci. & Eng., Tatung Univ., Taipei, Taiwan#TAB#","institution_ids":["https://openalex.org/I65196183"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102024244","display_name":"Chih-Lung Lin","orcid":"https://orcid.org/0000-0003-0831-8251"},"institutions":[{"id":"https://openalex.org/I88780834","display_name":"Hwa Hsia University of Technology","ror":"https://ror.org/02mfjgm25","country_code":"TW","type":"education","lineage":["https://openalex.org/I88780834"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Lung Lin","raw_affiliation_strings":["Department of of Electronic Engineering, Hwa Hsia Institute of Technology, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of of Electronic Engineering, Hwa Hsia Institute of Technology, Taiwan","institution_ids":["https://openalex.org/I88780834"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.0948023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"359","last_page":"363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.674322783946991},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.6009072065353394},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5518683195114136},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4560711979866028},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45547863841056824},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4377223253250122},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4239887595176697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.354790598154068},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33949995040893555},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19865697622299194},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18022498488426208}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.674322783946991},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.6009072065353394},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5518683195114136},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4560711979866028},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45547863841056824},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4377223253250122},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4239887595176697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.354790598154068},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33949995040893555},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19865697622299194},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18022498488426208},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2014.7009142","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2014.7009142","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321040","display_name":"National Science Council","ror":"https://ror.org/02kv4zf79"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1551545792","https://openalex.org/W1997733123","https://openalex.org/W2041390734","https://openalex.org/W2065994824","https://openalex.org/W2088205507","https://openalex.org/W2102509024","https://openalex.org/W2125793385","https://openalex.org/W2158437588","https://openalex.org/W2164489414","https://openalex.org/W6675518992","https://openalex.org/W6683119752"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W2937099569","https://openalex.org/W3005992387"],"abstract_inverted_index":{"Crowd":[0],"management":[1,24],"research":[2],"shows":[3],"a":[4,22,62,80,106,110,148,162,187,213,241,279],"lack":[5],"of":[6,116,128,141,215,237,251,282,301,314],"depth":[7],"in":[8,109,124,205,233,256,267],"the":[9,36,46,54,67,85,125,132,136,138,142,170,173,182,193,200,206,221,223,230,234,248,252,257,264,268,293,299,312],"literature":[10],"insofar":[11],"as":[12,192],"most":[13],"major":[14],"incidents":[15],"can":[16,31,40,50,56,209,285],"be":[17,32,51,57,92,105,210],"prevented":[18,52],"or":[19,53,305],"minimized":[20],"by":[21,122,147,212],"proper":[23],"strategy.":[25],"Specifically,":[26],"if":[27],"abnormal":[28,158,194,315],"crowd":[29,69,86,159,163,175,183,195,203],"events":[30,160,262],"detected":[33],"early":[34],"and":[35,98,172,189,240,296],"relevant":[37],"governing":[38],"agency":[39],"take":[41],"appropriate":[42],"actions":[43],"towards":[44],"mitigating":[45],"dangers,":[47],"accidental":[48],"injury":[49],"incident":[55],"contained.":[58],"This":[59],"paper":[60],"presents":[61],"technical":[63],"approach":[64],"to":[65,74,83,104,165,167,185,199,228,246,291,310],"gather":[66],"required":[68],"data":[70,77],"using":[71,79],"fixed":[72],"cameras":[73],"collect":[75],"visual":[76],"while":[78],"grid":[81,111,207,235,269],"model":[82,208,236,270],"describe":[84,229,247],"distribution.":[87],"The":[88,113],"measured":[89,143],"area":[90,101,119,144,171],"will":[91,153,273],"divided":[93],"into":[94],"several":[95],"unit":[96,100,118,133],"areas":[97],"each":[99,117,238],"is":[102,120,145,226,244],"considered":[103],"simple":[107],"cell":[108],"model.":[112],"state":[114,150],"value":[115],"determined":[121],"changes":[123,177,277],"total":[126],"number":[127],"active":[129],"pixels":[130],"within":[131,278],"area.":[134],"Under":[135],"circumstances,":[137],"motion":[139],"status":[140,231,265],"represented":[146],"dynamic":[149],"matrix,":[151],"which":[152],"save":[154],"computing":[155],"time.":[156,283],"Should":[157],"develop,":[161],"tends":[164],"attempt":[166],"quickly":[168],"leave":[169],"resultant":[174],"distribution":[176,184,204,232,266],"accordingly.":[178],"Thus,":[179],"we":[180],"expect":[181],"have":[186],"sudden":[188],"significant":[190],"change":[191],"event":[196],"unfolds.":[197],"According":[198],"Uniqueness":[201],"Theorem,":[202],"described":[211],"series":[214,243],"different":[216],"order":[217],"moments.":[218],"To":[219],"consider":[220],"normalization,":[222],"Chebyshev":[224],"moment":[225],"used":[227,245,309],"image":[239],"time":[242],"varying":[249],"features":[250],"two":[253],"adjacent":[254],"images":[255,272],"video":[258],"data.":[259],"When":[260],"unusual":[261],"occur,":[263],"between":[271],"show":[274],"more":[275],"obvious":[276],"short":[280],"period":[281],"We":[284],"use":[286],"an":[287],"outlier":[288],"detection":[289],"method":[290],"detect":[292],"extreme":[294],"values":[295],"also":[297],"identify":[298],"type":[300],"outlier:":[302],"additive":[303],"outliers":[304,307],"innovational":[306],"are":[308],"determine":[311],"cause":[313],"feature":[316],"variation.":[317]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}