An ensemble approach for increased anomaly detection performance in video surveillance data
The increased societal need for surveillance and the decrease in cost of sensors have led to a number of new challenges. The problem is not to collect data but to use it effectively for decision support. Manual interpretation of huge amounts of data in real-time is not feasible; the operator of a su...
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| creator | Brax, C. Niklasson, L. Laxhammar, R. |
| description | The increased societal need for surveillance and the decrease in cost of sensors have led to a number of new challenges. The problem is not to collect data but to use it effectively for decision support. Manual interpretation of huge amounts of data in real-time is not feasible; the operator of a surveillance system needs support to analyze and understand all incoming data. In this paper an approach to intelligent video surveillance is presented, with emphasis on finding behavioural anomalies. Two different anomaly detection methods are compared and combined. The results show that it is possible to best increase the total detection performance by combining two different anomaly detectors rather than employing them independently. |
| format | Conference Proceeding |
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The results show that it is possible to best increase the total detection performance by combining two different anomaly detectors rather than employing them independently.</description><subject>anomaly detection</subject><subject>behaviour classification</subject><subject>Cameras</subject><subject>CCTV</subject><subject>classifier fusion</subject><subject>Costs</subject><subject>Detection algorithms</subject><subject>Detectors</subject><subject>Explosions</subject><subject>Informatics</subject><subject>Real time systems</subject><subject>Sensor fusion</subject><subject>Technology</subject><subject>Teknik</subject><subject>Terrorism</subject><subject>video content analysis</subject><subject>Video surveillance</subject><isbn>9780982443804</isbn><isbn>0982443803</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9zE1LAzEUheEBEZTaX-Ameynkc3JnWeonFNyoGxfDTXLHRmYmQzKt9N9bVFwdeHk4Z9WyscAbkFor4PqiWpbyyTkXTW0F8MvqfT0yGgsNrieG05QT-h3rUmZx9JmwUGA4pgH7Iws0k59jGtlE-UQGHD2dHDvEQImVfT5Q7PufGnDGq-q8w77Q8m8X1ev93cvmcbV9fnjarLerKIWYV9ABBADr0HLhvQykjXSN1l6qOgTnnJZWC9dJA87USliOAmwnOzLWGFSL6ub3t3zRtHftlOOA-dgmjO1tfFu3KX-0u1hapYU66etfHYno3xrJFUCtvgE8Ilzm</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Brax, C.</creator><creator>Niklasson, L.</creator><creator>Laxhammar, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>ADTPV</scope><scope>BNKNJ</scope><scope>DF6</scope></search><sort><creationdate>200907</creationdate><title>An ensemble approach for increased anomaly detection performance in video surveillance data</title><author>Brax, C. ; Niklasson, L. ; Laxhammar, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i211t-8f88d887ba701cc2de452b944c236ddbbb42741bf258b563170a187f2fe5755a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>anomaly detection</topic><topic>behaviour classification</topic><topic>Cameras</topic><topic>CCTV</topic><topic>classifier fusion</topic><topic>Costs</topic><topic>Detection algorithms</topic><topic>Detectors</topic><topic>Explosions</topic><topic>Informatics</topic><topic>Real time systems</topic><topic>Sensor fusion</topic><topic>Technology</topic><topic>Teknik</topic><topic>Terrorism</topic><topic>video content analysis</topic><topic>Video surveillance</topic><toplevel>online_resources</toplevel><creatorcontrib>Brax, C.</creatorcontrib><creatorcontrib>Niklasson, L.</creatorcontrib><creatorcontrib>Laxhammar, R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>SwePub</collection><collection>SwePub Conference</collection><collection>SWEPUB Högskolan i Skövde</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Brax, C.</au><au>Niklasson, L.</au><au>Laxhammar, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An ensemble approach for increased anomaly detection performance in video surveillance data</atitle><btitle>2009 12th International Conference on Information Fusion</btitle><stitle>ICIF</stitle><date>2009-07</date><risdate>2009</risdate><spage>694</spage><epage>701</epage><pages>694-701</pages><isbn>9780982443804</isbn><isbn>0982443803</isbn><abstract>The increased societal need for surveillance and the decrease in cost of sensors have led to a number of new challenges. The problem is not to collect data but to use it effectively for decision support. Manual interpretation of huge amounts of data in real-time is not feasible; the operator of a surveillance system needs support to analyze and understand all incoming data. In this paper an approach to intelligent video surveillance is presented, with emphasis on finding behavioural anomalies. Two different anomaly detection methods are compared and combined. The results show that it is possible to best increase the total detection performance by combining two different anomaly detectors rather than employing them independently.</abstract><pub>IEEE</pub><tpages>8</tpages></addata></record> |
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| identifier | ISBN: 9780982443804 |
| ispartof | 2009 12th International Conference on Information Fusion, 2009, p.694-701 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | anomaly detection behaviour classification Cameras CCTV classifier fusion Costs Detection algorithms Detectors Explosions Informatics Real time systems Sensor fusion Technology Teknik Terrorism video content analysis Video surveillance |
| title | An ensemble approach for increased anomaly detection performance in video surveillance data |
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