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An ensemble approac...
An ensemble approach for increased anomaly detection performance in video surveillance data
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- Brax, Christoffer (författare)
- Högskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
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- Niklasson, Lars (författare)
- Högskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
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- Laxhammar, Rikard (författare)
- Högskolan i Skövde,Institutionen för kommunikation och information,Forskningscentrum för Informationsteknologi,Skövde Artificial Intelligence Lab (SAIL)
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(creator_code:org_t)
- IEEE conference proceedings, 2009
- 2009
- Engelska.
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Ingår i: Proceedings of the 12th International Conference on Information Fusion (FUSION 2009), Seattle, Washington, USA, 6–9 July 2009. - : IEEE conference proceedings. - 9780982443804 ; , s. 694-701
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- anomaly detection
- classifier fusion
- CCTV
- video content analysis
- behaviour classification
- Technology
- Teknik
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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