An ensemble approach for increased anomaly detection performance in video surveillance data
2009 (English)In: Proceedings of the 12th International Conference on Information Fusion (FUSION 2009), Seattle, Washington, USA, 6–9 July 2009, IEEE conference proceedings, 2009, p. 694-701Conference paper, Published paper (Refereed)
Abstract [en]
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.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2009. p. 694-701
Keywords [en]
anomaly detection, classifier fusion, CCTV, video content analysis, behaviour classification
National Category
Computer Sciences
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3413ISI: 000273560000090Scopus ID: 2-s2.0-70449359707ISBN: 978-0-9824438-0-4 (print)OAI: oai:DiVA.org:his-3413DiVA, id: diva2:249226
Conference
Fusion 2009 : the 12th International Conference on Information Fusion : Grand Hyatt Seattle, Seattle, Washington, USA, 6-9 July, 2009
2009-10-092009-10-092021-11-17Bibliographically approved