A hybrid architecture for intelligent video surveillance

Author

Massa, V. Di ; Gori, M. ; Russo, I.

Author_Institution

Dept. of Inf. Eng., Siena Univ.

fYear

2005

fDate

March 31 2005-April 1 2005

Firstpage

90

Lastpage

93

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CIHSPS.2005.1500618

Filename

1500618