Title :
A hybrid architecture for intelligent video surveillance
Author :
Massa, V. Di ; Gori, M. ; Russo, I.
Author_Institution :
Dept. of Inf. Eng., Siena Univ.
fDate :
March 31 2005-April 1 2005
Abstract :
This paper presents a hybrid architecture for intelligent video surveillance which is able to detect complex events on the basis of a strongly-based learning approach. We describe briefly the main components used for motion detection, segmentation, tracking, and clustering, along with the solution adopted for their hybrid combination. Finally, we emphasize the approach adopted for classifying video sequences which is based on hidden Markov models
Keywords :
hidden Markov models; image motion analysis; image segmentation; image sequences; knowledge based systems; learning (artificial intelligence); pattern classification; surveillance; hidden Markov models; intelligent video surveillance; motion clustering; motion detection; motion segmentation; motion tracking; video sequence classification; Competitive intelligence; Costs; Event detection; Hidden Markov models; Humans; Layout; Motion detection; Tracking; Video sequences; Video surveillance;
Conference_Titel :
Computational Intelligence for Homeland Security and Personal Safety, 2005. CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9176-4
DOI :
10.1109/CIHSPS.2005.1500618
