{"id":"https://openalex.org/W2601635289","doi":"https://doi.org/10.5220/0006116902100217","title":"Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments","display_name":"Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2601635289","doi":"https://doi.org/10.5220/0006116902100217","mag":"2601635289"},"language":"en","primary_location":{"id":"doi:10.5220/0006116902100217","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0006116902100217","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0006116902100217","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072636788","display_name":"Dario Dotti","orcid":"https://orcid.org/0000-0001-5798-4256"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Dario Dotti","raw_affiliation_strings":["Maastricht University, Netherlands"],"affiliations":[{"raw_affiliation_string":"Maastricht University, Netherlands","institution_ids":["https://openalex.org/I34352273"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015435362","display_name":"Mirela Popa","orcid":"https://orcid.org/0000-0002-6449-1158"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mirela Popa","raw_affiliation_strings":["Maastricht University, Netherlands"],"affiliations":[{"raw_affiliation_string":"Maastricht University, Netherlands","institution_ids":["https://openalex.org/I34352273"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040216921","display_name":"Stylianos Asteriadis","orcid":"https://orcid.org/0000-0002-4298-6870"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Stylianos Asteriadis","raw_affiliation_strings":["Maastricht University, Netherlands"],"affiliations":[{"raw_affiliation_string":"Maastricht University, Netherlands","institution_ids":["https://openalex.org/I34352273"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5072636788"],"corresponding_institution_ids":["https://openalex.org/I34352273"],"apc_list":null,"apc_paid":null,"fwci":1.3651,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.85398092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"210","last_page":"217"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9973000288009644,"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"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9764000177383423,"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":0.6031913161277771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3453022837638855}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6031913161277771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3453022837638855}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0006116902100217","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0006116902100217","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.maastrichtuniversity.nl:openaire/96884540-c62a-4801-b244-2296910d10db","is_oa":false,"landing_page_url":"https://cris.maastrichtuniversity.nl/en/publications/96884540-c62a-4801-b244-2296910d10db","pdf_url":null,"source":{"id":"https://openalex.org/S4306402616","display_name":"Research Publications (Maastricht University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I34352273","host_organization_name":"Maastricht University","host_organization_lineage":["https://openalex.org/I34352273"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Dotti, D, Popa, M & Asteriadis, S 2017, Unsupervised Discovery of Normal and Abnormal Activity Patterns in Indoor and Outdoor Environments. in VISAPP 2017 12th International Conference on Computer Vision Theory and Applications, Porto, Portugal, 27 February - 1 March 2017. SCITEPRESS, pp. 210-217, 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), Porto, Portugal, 27/02/17. https://doi.org/10.5220/0006116902100217","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.5220/0006116902100217","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0006116902100217","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.4000000059604645,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1540959951","https://openalex.org/W1945475446","https://openalex.org/W1952703253","https://openalex.org/W1967456674","https://openalex.org/W1982692259","https://openalex.org/W1999499433","https://openalex.org/W2018098839","https://openalex.org/W2110940013","https://openalex.org/W2126484555","https://openalex.org/W2136655611","https://openalex.org/W2146521180","https://openalex.org/W2165131075","https://openalex.org/W2184188583","https://openalex.org/W2403635349","https://openalex.org/W3141200356","https://openalex.org/W3151123641"],"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],"paper":[2],"we":[3,53],"propose":[4],"an":[5,80,107],"adaptive":[6],"system":[7,18],"for":[8,119],"monitoring":[9],"indoor":[10,92],"and":[11,24,43,68,71,93],"outdoor":[12,94],"environments":[13],"using":[14,83],"movement":[15],"patterns.":[16],"Our":[17],"is":[19,134],"able":[20],"to":[21],"discover":[22],"normal":[23,114],"abnormal":[25,116],"activity":[26,75,117],"patterns":[27,118],"in":[28,79,112],"absence":[29],"of":[30,102,109,126,131,139,145],"any":[31],"prior":[32],"knowledge.":[33],"We":[34],"employ":[35],"several":[36],"feature":[37,57],"descriptors,":[38],"by":[39,59],"extracting":[40],"both":[41,91,120],"spatial":[42,50],"temporal":[44],"cues":[45],"from":[46],"trajectories":[47],"over":[48,110],"a":[49,123],"grid.":[51],"Moreover,":[52],"improve":[54],"the":[55,100,103,127,132,137,143],"initial":[56],"vectors":[58],"applying":[60],"sparse":[61],"autoencoders,":[62],"which":[63],"help":[64],"at":[65],"obtaining":[66],"optimized":[67],"compact":[69],"representations":[70],"improved":[72],"accuracy.":[73],"Next,":[74],"models":[76],"are":[77,88],"learnt":[78],"unsupervised":[81],"manner":[82],"clustering":[84],"techniques.":[85],"The":[86,96],"experiments":[87],"performed":[89],"on":[90],"datasets.":[95],"obtained":[97,135],"results":[98],"prove":[99],"suitability":[101],"proposed":[104],"system,":[105],"achieving":[106],"accuracy":[108],"98%":[111],"classifying":[113],"vs.":[115],"scenarios.":[121],"Furthermore,":[122],"semantic":[124],"interpretation":[125],"most":[128],"important":[129],"regions":[130],"scene":[133],"without":[136],"need":[138],"human":[140],"labels,":[141],"highlighting":[142],"flexibility":[144],"our":[146],"method.":[147]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
