{"id":"https://openalex.org/W4308097563","doi":"https://doi.org/10.1109/euvip53989.2022.9922843","title":"Change Detection in Moving-Camera Videos Using a Shift-Invariant Dissimilarity Metric","display_name":"Change Detection in Moving-Camera Videos Using a Shift-Invariant Dissimilarity Metric","publication_year":2022,"publication_date":"2022-09-11","ids":{"openalex":"https://openalex.org/W4308097563","doi":"https://doi.org/10.1109/euvip53989.2022.9922843"},"language":"en","primary_location":{"id":"doi:10.1109/euvip53989.2022.9922843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip53989.2022.9922843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th European Workshop on Visual Information Processing (EUVIP)","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/A5023506755","display_name":"Thadeu L. B. Dias","orcid":"https://orcid.org/0000-0003-3371-5291"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Thadeu L. B. Dias","raw_affiliation_strings":["Federal University of Rio de Janeiro,Electrical Engineering Program, COPPE UFRJ,Rio de Janeiro,Brazil","Electrical Engineering Program, COPPE UFRJ, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Rio de Janeiro,Electrical Engineering Program, COPPE UFRJ,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I122140584"]},{"raw_affiliation_string":"Electrical Engineering Program, COPPE UFRJ, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081219742","display_name":"Eduardo A. B. da Silva","orcid":"https://orcid.org/0000-0001-7755-6988"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eduardo A. B. Silva","raw_affiliation_strings":["Federal University of Rio de Janeiro,Electrical Engineering Program, COPPE UFRJ,Rio de Janeiro,Brazil","Electrical Engineering Program, COPPE UFRJ, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Rio de Janeiro,Electrical Engineering Program, COPPE UFRJ,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I122140584"]},{"raw_affiliation_string":"Electrical Engineering Program, COPPE UFRJ, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002277423","display_name":"S\u00e9rgio L. Netto","orcid":"https://orcid.org/0000-0001-7389-1463"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Sergio L. Netto","raw_affiliation_strings":["Federal University of Rio de Janeiro,Electrical Engineering Program, COPPE UFRJ,Rio de Janeiro,Brazil","Electrical Engineering Program, COPPE UFRJ, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Rio de Janeiro,Electrical Engineering Program, COPPE UFRJ,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I122140584"]},{"raw_affiliation_string":"Electrical Engineering Program, COPPE UFRJ, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012375351","display_name":"Lucas A. Thomaz","orcid":"https://orcid.org/0000-0002-1004-7772"},"institutions":[{"id":"https://openalex.org/I169427155","display_name":"Instituto Polit\u00e9cnico de Leiria","ror":"https://ror.org/010dvvh94","country_code":"PT","type":"education","lineage":["https://openalex.org/I169427155"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Lucas A. Thomaz","raw_affiliation_strings":["ESTG, Polytechnic of Leiria,Instituto de Telecomunicações, Portugal,Leiria,Portugal"],"affiliations":[{"raw_affiliation_string":"ESTG, Polytechnic of Leiria,Instituto de Telecomunicações, Portugal,Leiria,Portugal","institution_ids":["https://openalex.org/I169427155"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023506755"],"corresponding_institution_ids":["https://openalex.org/I122140584"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53750698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12391","display_name":"Artificial Immune Systems Applications","score":0.9628000259399414,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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":0.7588584423065186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7476201057434082},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7170788049697876},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5722116231918335},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5384296178817749},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.533811628818512},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5257611870765686},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.522239089012146},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4414924085140228},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4238007962703705},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38094741106033325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09951713681221008}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7588584423065186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7476201057434082},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7170788049697876},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5722116231918335},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5384296178817749},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.533811628818512},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5257611870765686},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.522239089012146},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4414924085140228},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4238007962703705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38094741106033325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09951713681221008},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/euvip53989.2022.9922843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip53989.2022.9922843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 10th European Workshop on Visual Information Processing (EUVIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4287995534","https://openalex.org/W2998168123","https://openalex.org/W2939353110","https://openalex.org/W2897995864","https://openalex.org/W3044458868","https://openalex.org/W2587789887","https://openalex.org/W3176919784","https://openalex.org/W4300631627","https://openalex.org/W2953238046"],"abstract_inverted_index":{"This":[0],"work":[1],"addresses":[2],"the":[3,24,32,37,40,65,68,71,125,141,144,149,162,196,207,213,217],"problem":[4],"of":[5,26,39,42,70,85,133,153,200,204,206,216,228,235],"identifying":[6],"anomalous":[7,86],"objects":[8],"in":[9,23,36,88,94,101,115,187],"a":[10,56,95,116,128,157,175],"cluttered":[11],"environment":[12],"using":[13,140],"footage":[14],"captured":[15],"with":[16,131,189,220],"moving":[17],"cameras.":[18],"The":[19,98,119],"proposed":[20],"method":[21,121],"consists":[22],"application":[25,114],"an":[27,61,168,231],"edge-like":[28],"feature":[29],"extractor":[30],"on":[31,106,124],"input":[33],"images":[34],"and":[35,80,155,230],"learning":[38],"space":[41],"non-anomalous":[43],"features":[44],"through":[45],"deep":[46,92],"autoencoder":[47,145],"networks.":[48],"Interestingly,":[49],"this":[50,102,180,210],"process":[51],"is":[52,76],"equivalent":[53],"to":[54,64,78,90,161,224],"employing":[55],"shift-invariant":[57],"dissimilarity":[58],"metric":[59],"as":[60],"optimization":[62],"target":[63],"autoencoders.":[66],"By":[67],"nature":[69],"change":[72],"detection":[73],"task,":[74],"it":[75],"necessary":[77],"collect":[79],"annotate":[81],"large,":[82],"diverse":[83],"sets":[84],"conditions":[87],"order":[89],"train":[91],"models":[93,99],"supervised":[96],"manner.":[97],"trained":[100,123],"proposal":[103,181,211],"rely":[104],"solely":[105],"anomalous-free":[107],"data":[108],"for":[109,183],"parameter":[110],"training,":[111],"facilitating":[112],"its":[113],"real-world":[117],"scenario.":[118],"developed":[120],"was":[122],"VDAO":[126,208],"dataset,":[127,198],"challenging":[129],"dataset":[130],"recordings":[132,205],"varied":[134],"illumination":[135],"situations.":[136],"Unlike":[137],"previous":[138],"works":[139],"same":[142],"set,":[143],"does":[146],"not":[147,171],"require":[148],"computationally":[150],"expensive":[151],"step":[152],"matching":[154],"registering":[156],"known":[158],"reference":[159],"frame":[160,164],"tested":[163,194],"that":[165],"potentially":[166],"contains":[167],"anomaly.":[169],"Requiring":[170],"much":[172],"more":[173],"than":[174],"single":[176],"network":[177],"inference":[178],"step,":[179],"allows":[182],"real-time":[184],"execution,":[185],"even":[186],"systems":[188],"modest":[190],"computational":[191],"power.":[192],"When":[193],"against":[195],"VDAO200":[197],"consisting":[199],"56":[201],"short":[202],"excerpts":[203],"scenes,":[209],"matches":[212],"evaluation":[214],"figures":[215],"state-of-the-art":[218],"algorithms,":[219],"measured":[221],"DIS":[222],"(distance":[223],"upper-left":[225],"ROC":[226],"corner)":[227],"0.29":[229],"average":[232],"precision":[233],"score":[234],"0.91.":[236]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}