Abnormal behavior detection using dominant sets
Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent b...
Gespeichert in:
| Veröffentlicht in: | Machine vision and applications 2014-07, Vol.25 (5), p.1351-1368 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1368 |
|---|---|
| container_issue | 5 |
| container_start_page | 1351 |
| container_title | Machine vision and applications |
| container_volume | 25 |
| creator | Alvar, Manuel Torsello, Andrea Sanchez-Miralles, Alvaro Armingol, José María |
| description | Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy
K
-Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least
1.5
points lower than the others. |
| doi_str_mv | 10.1007/s00138-014-0615-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1559713403</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1559713403</sourcerecordid><originalsourceid>FETCH-LOGICAL-c382t-9636c0236992d73b0996ac3ec4c92bf79914d4b96ddf9d412cd06084f7b629883</originalsourceid><addsrcrecordid>eNp1kE1LxDAQhoMouK7-AG8FL17qTj426RyXxS9Y8KLn0Cbp2qVN1qQV_Pe2VBAEmcPM4XlfhoeQawp3FECtEgDlRQ5U5CDpOhcnZEEFZzlVEk_JAnC8C0B2Ti5SOgCAUEosyGpT-RC7ss0q915-NiFm1vXO9E3w2ZAav89s6Bpf-j5Lrk-X5Kwu2-SufvaSvD3cv26f8t3L4_N2s8sNL1ifo-TSAOMSkVnFK0CUpeHOCIOsqhUiFVZUKK2t0QrKjAUJhahVJRkWBV-S27n3GMPH4FKvuyYZ17ald2FImq7XqCgXwEf05g96CEP043eaMTlOIUCMFJ0pE0NK0dX6GJuujF-agp4U6lmhHhXqSaGeMmzOpJH1exd_m_8PfQOjTHFR</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2262628404</pqid></control><display><type>article</type><title>Abnormal behavior detection using dominant sets</title><source>SpringerLink Journals - AutoHoldings</source><creator>Alvar, Manuel ; Torsello, Andrea ; Sanchez-Miralles, Alvaro ; Armingol, José María</creator><creatorcontrib>Alvar, Manuel ; Torsello, Andrea ; Sanchez-Miralles, Alvaro ; Armingol, José María</creatorcontrib><description>Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy
K
-Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least
1.5
points lower than the others.</description><identifier>ISSN: 0932-8092</identifier><identifier>EISSN: 1432-1769</identifier><identifier>DOI: 10.1007/s00138-014-0615-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Behavior ; Communications Engineering ; Computer Science ; Dangerous ; Error detection ; Fuzzy ; Fuzzy logic ; Fuzzy set theory ; Gaussian ; Human behavior ; Image Processing and Computer Vision ; Machine vision ; Networks ; Original Paper ; Pattern Recognition ; Robustness ; Robustness (mathematics) ; Surveillance systems ; Vision systems</subject><ispartof>Machine vision and applications, 2014-07, Vol.25 (5), p.1351-1368</ispartof><rights>Springer-Verlag Berlin Heidelberg 2014</rights><rights>Machine Vision and Applications is a copyright of Springer, (2014). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-9636c0236992d73b0996ac3ec4c92bf79914d4b96ddf9d412cd06084f7b629883</citedby><cites>FETCH-LOGICAL-c382t-9636c0236992d73b0996ac3ec4c92bf79914d4b96ddf9d412cd06084f7b629883</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00138-014-0615-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00138-014-0615-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Alvar, Manuel</creatorcontrib><creatorcontrib>Torsello, Andrea</creatorcontrib><creatorcontrib>Sanchez-Miralles, Alvaro</creatorcontrib><creatorcontrib>Armingol, José María</creatorcontrib><title>Abnormal behavior detection using dominant sets</title><title>Machine vision and applications</title><addtitle>Machine Vision and Applications</addtitle><description>Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy
K
-Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least
1.5
points lower than the others.</description><subject>Behavior</subject><subject>Communications Engineering</subject><subject>Computer Science</subject><subject>Dangerous</subject><subject>Error detection</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Gaussian</subject><subject>Human behavior</subject><subject>Image Processing and Computer Vision</subject><subject>Machine vision</subject><subject>Networks</subject><subject>Original Paper</subject><subject>Pattern Recognition</subject><subject>Robustness</subject><subject>Robustness (mathematics)</subject><subject>Surveillance systems</subject><subject>Vision systems</subject><issn>0932-8092</issn><issn>1432-1769</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kE1LxDAQhoMouK7-AG8FL17qTj426RyXxS9Y8KLn0Cbp2qVN1qQV_Pe2VBAEmcPM4XlfhoeQawp3FECtEgDlRQ5U5CDpOhcnZEEFZzlVEk_JAnC8C0B2Ti5SOgCAUEosyGpT-RC7ss0q915-NiFm1vXO9E3w2ZAav89s6Bpf-j5Lrk-X5Kwu2-SufvaSvD3cv26f8t3L4_N2s8sNL1ifo-TSAOMSkVnFK0CUpeHOCIOsqhUiFVZUKK2t0QrKjAUJhahVJRkWBV-S27n3GMPH4FKvuyYZ17ald2FImq7XqCgXwEf05g96CEP043eaMTlOIUCMFJ0pE0NK0dX6GJuujF-agp4U6lmhHhXqSaGeMmzOpJH1exd_m_8PfQOjTHFR</recordid><startdate>20140701</startdate><enddate>20140701</enddate><creator>Alvar, Manuel</creator><creator>Torsello, Andrea</creator><creator>Sanchez-Miralles, Alvaro</creator><creator>Armingol, José María</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140701</creationdate><title>Abnormal behavior detection using dominant sets</title><author>Alvar, Manuel ; Torsello, Andrea ; Sanchez-Miralles, Alvaro ; Armingol, José María</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-9636c0236992d73b0996ac3ec4c92bf79914d4b96ddf9d412cd06084f7b629883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Behavior</topic><topic>Communications Engineering</topic><topic>Computer Science</topic><topic>Dangerous</topic><topic>Error detection</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>Gaussian</topic><topic>Human behavior</topic><topic>Image Processing and Computer Vision</topic><topic>Machine vision</topic><topic>Networks</topic><topic>Original Paper</topic><topic>Pattern Recognition</topic><topic>Robustness</topic><topic>Robustness (mathematics)</topic><topic>Surveillance systems</topic><topic>Vision systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alvar, Manuel</creatorcontrib><creatorcontrib>Torsello, Andrea</creatorcontrib><creatorcontrib>Sanchez-Miralles, Alvaro</creatorcontrib><creatorcontrib>Armingol, José María</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Machine vision and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alvar, Manuel</au><au>Torsello, Andrea</au><au>Sanchez-Miralles, Alvaro</au><au>Armingol, José María</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Abnormal behavior detection using dominant sets</atitle><jtitle>Machine vision and applications</jtitle><stitle>Machine Vision and Applications</stitle><date>2014-07-01</date><risdate>2014</risdate><volume>25</volume><issue>5</issue><spage>1351</spage><epage>1368</epage><pages>1351-1368</pages><issn>0932-8092</issn><eissn>1432-1769</eissn><abstract>Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy
K
-Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least
1.5
points lower than the others.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00138-014-0615-4</doi><tpages>18</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0932-8092 |
| ispartof | Machine vision and applications, 2014-07, Vol.25 (5), p.1351-1368 |
| issn | 0932-8092 1432-1769 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1559713403 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Behavior Communications Engineering Computer Science Dangerous Error detection Fuzzy Fuzzy logic Fuzzy set theory Gaussian Human behavior Image Processing and Computer Vision Machine vision Networks Original Paper Pattern Recognition Robustness Robustness (mathematics) Surveillance systems Vision systems |
| title | Abnormal behavior detection using dominant sets |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T14%3A05%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Abnormal%20behavior%20detection%20using%20dominant%20sets&rft.jtitle=Machine%20vision%20and%20applications&rft.au=Alvar,%20Manuel&rft.date=2014-07-01&rft.volume=25&rft.issue=5&rft.spage=1351&rft.epage=1368&rft.pages=1351-1368&rft.issn=0932-8092&rft.eissn=1432-1769&rft_id=info:doi/10.1007/s00138-014-0615-4&rft_dat=%3Cproquest_cross%3E1559713403%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2262628404&rft_id=info:pmid/&rfr_iscdi=true |