{"id":"https://openalex.org/W3020935318","doi":"https://doi.org/10.1145/3293353.3293354","title":"PredGAN","display_name":"PredGAN","publication_year":2018,"publication_date":"2018-12-18","ids":{"openalex":"https://openalex.org/W3020935318","doi":"https://doi.org/10.1145/3293353.3293354","mag":"3020935318"},"language":"en","primary_location":{"id":"doi:10.1145/3293353.3293354","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3293353.3293354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing","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/A5073121051","display_name":"Adarsh Jamadandi","orcid":"https://orcid.org/0000-0002-0267-2244"},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Adarsh Jamadandi","raw_affiliation_strings":["B.V.Bhoomaraddi College of Engineering and Technology, Hubli, Karnataka"],"affiliations":[{"raw_affiliation_string":"B.V.Bhoomaraddi College of Engineering and Technology, Hubli, Karnataka","institution_ids":["https://openalex.org/I3132975163"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049347764","display_name":"Sunidhi Kotturshettar","orcid":null},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sunidhi Kotturshettar","raw_affiliation_strings":["B.V.Bhoomaraddi College of Engineering and Technology, Hubli, Karnataka"],"affiliations":[{"raw_affiliation_string":"B.V.Bhoomaraddi College of Engineering and Technology, Hubli, Karnataka","institution_ids":["https://openalex.org/I3132975163"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031053524","display_name":"Uma Mudenagudi","orcid":"https://orcid.org/0000-0003-1111-7522"},"institutions":[{"id":"https://openalex.org/I3132975163","display_name":"KLE Technological University","ror":"https://ror.org/04yh52k23","country_code":"IN","type":"education","lineage":["https://openalex.org/I3132975163"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Uma Mudenagudi","raw_affiliation_strings":["B.V.Bhoomaraddi College of Engineering and Technology, Hubli, Karnataka"],"affiliations":[{"raw_affiliation_string":"B.V.Bhoomaraddi College of Engineering and Technology, Hubli, Karnataka","institution_ids":["https://openalex.org/I3132975163"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073121051"],"corresponding_institution_ids":["https://openalex.org/I3132975163"],"apc_list":null,"apc_paid":null,"fwci":0.4887,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75020337,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"36","issue":null,"first_page":"1","last_page":"8"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9521999955177307,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.933899998664856,"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":0.46889299154281616}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46889299154281616}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3293353.3293354","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3293353.3293354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1581676061","https://openalex.org/W1967456674","https://openalex.org/W2012931101","https://openalex.org/W2053765339","https://openalex.org/W2122361470","https://openalex.org/W2125101937","https://openalex.org/W2143668817","https://openalex.org/W2144846366","https://openalex.org/W2248556341","https://openalex.org/W2432004435","https://openalex.org/W2481240925","https://openalex.org/W2559927751","https://openalex.org/W2768426221","https://openalex.org/W2777556841","https://openalex.org/W2791184993","https://openalex.org/W2950773078","https://openalex.org/W2951523806","https://openalex.org/W2963917315","https://openalex.org/W2990138404"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655"],"abstract_inverted_index":{"In":[0],"this":[1,50,66],"paper":[2],"we":[3,52,192],"propose":[4,53],"a":[5,40,141,146,169],"multi-scale":[6],"video":[7],"prediction":[8],"framework":[9,31,161,184],"with":[10],"adversarial":[11],"training":[12],"for":[13,34],"detecting":[14],"anomalies":[15],"in":[16],"videos.":[17,75],"Anomalous":[18],"events":[19],"are":[20,107,128],"those":[21],"which":[22],"do":[23],"not":[24],"conform":[25],"to":[26,57,68,117,130,144,173],"normal":[27,86],"behavior.":[28],"Supervised":[29],"learning":[30,67],"cannot":[32,45],"account":[33],"all":[35],"the":[36,59,71,74,96,104,114,122,159,175,178],"unusual":[37,92],"activities":[38,95,106],"since":[39],"universal":[41],"definition":[42],"of":[43,62,73,84,100,121,158,177],"anomaly":[44],"be":[46],"adopted.":[47],"To":[48],"tackle":[49],"problem,":[51],"an":[54],"unsupervised":[55],"approach":[56],"learn":[58],"internal":[60],"representation":[61],"videos":[63,82],"and":[64,135,137,155,162,189],"use":[65],"accurately":[69],"predict":[70],"future-frames":[72],"We":[76,152,181],"train":[77],"our":[78,89,183],"network":[79,90],"adversarially":[80],"on":[81,185],"consisting":[83],"only":[85],"activities.":[87],"When":[88],"encounters":[91],"or":[93,150],"irregular":[94,105],"generated":[97,123],"frames":[98],"consists":[99],"fuzzy":[101,110],"regions":[102,111],"where":[103],"present.":[108],"These":[109],"consequently":[112],"lower":[113],"peak":[115],"signal":[116],"noise":[118],"ratio":[119],"(PSNR)":[120],"frames.":[124],"The":[125],"PSNR":[126],"values":[127,132],"normalized":[129],"have":[131],"between":[133],"0":[134],"1":[136],"is":[138],"used":[139],"as":[140,148,168],"regularity":[142],"score":[143],"tag":[145],"frame":[147],"anomalous":[149],"not-anomalous.":[151],"provide":[153],"quantitative":[154],"qualitative":[156],"evaluation":[157,171],"proposed":[160],"also":[163],"introduce":[164],"Earth":[165],"Mover's":[166],"Distance":[167],"new":[170],"metric":[172],"assess":[174],"quality":[176],"images":[179],"generated.":[180],"demonstrate":[182],"UCSD":[186],"Pedestrian":[187],"dataset":[188],"show":[190],"that":[191],"achieve":[193],"comparable":[194],"results.":[195]},"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":"2020-05-13T00:00:00"}