{"id":"https://openalex.org/W2062995043","doi":"https://doi.org/10.1109/icip.2014.7025469","title":"Simultaneous sparsity model for multi-perspective video anomaly detection","display_name":"Simultaneous sparsity model for multi-perspective video anomaly detection","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2062995043","doi":"https://doi.org/10.1109/icip.2014.7025469","mag":"2062995043"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2014.7025469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5011076660","display_name":"Xuan Mo","orcid":"https://orcid.org/0000-0001-6858-9307"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuan Mo","raw_affiliation_strings":["Department of Electrical Engineering, Pennsylvania State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Pennsylvania State University, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014013504","display_name":"Vishal Monga","orcid":"https://orcid.org/0000-0002-5100-2263"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vishal Monga","raw_affiliation_strings":["Department of Electrical Engineering, Pennsylvania State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Pennsylvania State University, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003241140","display_name":"Raja Bala","orcid":"https://orcid.org/0000-0002-0142-9859"},"institutions":[{"id":"https://openalex.org/I173498003","display_name":"Palo Alto Research Center","ror":"https://ror.org/0529fxt39","country_code":"US","type":"facility","lineage":["https://openalex.org/I173498003","https://openalex.org/I4210132870"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raja Bala","raw_affiliation_strings":["Xerox Research Center, Webster, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xerox Research Center, Webster, NY, USA","institution_ids":["https://openalex.org/I173498003"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2314","last_page":"2318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9970999956130981,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7552456855773926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6398206949234009},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6332197785377502},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5991466641426086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5867295861244202},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.5808197259902954},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5629715919494629},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5483971834182739},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4898214042186737},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4456862211227417},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.416352778673172},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4158550798892975},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3735814094543457},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.20585286617279053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7552456855773926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6398206949234009},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6332197785377502},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5991466641426086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5867295861244202},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.5808197259902954},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5629715919494629},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5483971834182739},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4898214042186737},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4456862211227417},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.416352778673172},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4158550798892975},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3735814094543457},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.20585286617279053},{"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/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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2014.7025469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"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/W1607828067","https://openalex.org/W1974774078","https://openalex.org/W1976058649","https://openalex.org/W1983008863","https://openalex.org/W2021659075","https://openalex.org/W2043702520","https://openalex.org/W2061572659","https://openalex.org/W2097511040","https://openalex.org/W2097915756","https://openalex.org/W2109389234","https://openalex.org/W2110934250","https://openalex.org/W2111918405","https://openalex.org/W2121167733","https://openalex.org/W2121194654","https://openalex.org/W2125276050","https://openalex.org/W2129812935","https://openalex.org/W2149021215","https://openalex.org/W2155763684","https://openalex.org/W2158694395","https://openalex.org/W6678461289"],"related_works":["https://openalex.org/W2011611369","https://openalex.org/W4297791310","https://openalex.org/W4372266926","https://openalex.org/W4245251483","https://openalex.org/W1998873033","https://openalex.org/W2116148865","https://openalex.org/W2366556501","https://openalex.org/W2965458591","https://openalex.org/W1520740474","https://openalex.org/W4254934694"],"abstract_inverted_index":{"Recently,":[0],"sparsity":[1,87,138],"based":[2,45,88,97,140],"classification":[3],"has":[4,49],"been":[5],"applied":[6],"to":[7,136,145,164,184],"video":[8,17,46,54,89,107,194],"anomaly":[9,47,55,90],"detection.":[10],"A":[11],"linear":[12,35],"model":[13],"is":[14,30,93,182],"assumed":[15],"over":[16,52],"features":[18],"(e.g.":[19],"trajectories)":[20],"such":[21,118],"that":[22,59,94,114,175,197,205],"the":[23,42,60,156,165,186],"feature":[24,39,102,143,151],"representation":[25,103],"of":[26,37,85,149,158],"a":[27,33,100,170,208],"new":[28],"event":[29,108,116,211],"written":[31],"as":[32,106,119],"sparse":[34,61,147,159],"combination":[36],"existing":[38,86],"representations":[40,62,117,144,148],"in":[41,58,69,80],"dictionary.":[43],"Sparsity":[44],"detection":[48,56,91],"shown":[50],"promise":[51],"alternate":[53],"methods":[57],"exhibit":[63],"excellent":[64],"robustness":[65],"under":[66],"noise":[67],"(common":[68],"surveillance":[70],"videos)":[71],"and":[72,122,169],"missing":[73],"or":[74,210],"corrupted":[75],"features,":[76],"e.g.":[77],"vehicle":[78],"occlusion":[79],"transportation":[81],"videos.":[82],"One":[83,110],"limitation":[84],"techniques":[92],"they":[95],"are":[96],"on":[98,141,191],"only":[99,207],"single":[101,142],"(known":[104],"formally":[105],"encoding).":[109],"can":[111],"easily":[112],"envision":[113],"different":[115],"object":[120],"trajectories":[121],"spatio-temporal":[123],"volumes":[124],"often":[125],"contain":[126],"correlated":[127],"yet":[128],"complementary":[129],"information.":[130],"In":[131,153],"this":[132,154],"paper,":[133],"we":[134],"propose":[135],"extend":[137],"models":[139],"simultaneous":[146,177],"multiple":[150],"representations.":[152],"model,":[155],"matrix":[157],"coefficients":[160],"does":[161],"not":[162],"confirm":[163],"commonly":[166],"seen":[167],"row-sparsity":[168],"modified":[171],"greedy":[172],"heuristic":[173],"approach":[174],"extends":[176],"orthogonal":[178],"matching":[179],"pursuit":[180],"(SOMP)":[181],"needed":[183],"solve":[185],"resulting":[187],"optimization":[188],"problem.":[189],"Experiments":[190],"two":[192],"benchmark":[193],"datasets":[195],"reveal":[196],"our":[198],"method":[199],"significantly":[200],"outperforms":[201],"state-of-the":[202],"art":[203],"approaches":[204],"utilize":[206],"single-perspective":[209],"encoding.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
