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 describes 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. © 2005 IEEE.
V., D.M., Gori, M., I., R. (2005). A hybrid architecture for intelligent video surveillance. In CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2005. (pp.90-93). New York USA : IEEE [10.1109/CIHSPS.2005.1500618].
A hybrid architecture for intelligent video surveillance
GORI, MARCO;
2005-01-01
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 describes 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. © 2005 IEEE.
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https://hdl.handle.net/11365/38539
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