Bayesian Framework
for Video Surveillance Application
Authors
Somboon Hongeng, François Brémond, Ramakant Nevatia
Abstract :
The goal of this paper is to describe and demonstrate the application of Bayesian
networks in a generic automatic video surveillance system. Taking image features
of tracked moving regions from an image sequence as input, mobile object properties
are first computed and noise is suppressed by statistical methods. The probability
that a scenario occurs is computed from these mobile object properties through
several layers of naive Bayesian classifiers (or a Bayesian network). Several
issues regarding the efficiency of the Bayesian network are discussed. For example,
the parameters of the networks, which represent rare activities (typical of video
surveillance application), can be learned from image sequences of similar scenarios
which are more common. We demonstrate the effectiveness of our approach by training
the networks with 600 image frames belonging to one domain of interest and applying
them to image sequences in a different domain
Keywords :
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BibTeX reference:
@ARTICLE{bre00b,
AUTHOR = {S. Hongeng and F. Br\'emond and R. Nevatia},
ADDRESS = {Barcelona ({S}pain)},
BOOKTITLE = {Proc. of the 15th {I}nternational {C}onference on {P}attern {R}ecognition},
TITLE = { Bayesian Framework for Video Surveillance Application},
MONTH = sep, YEAR = {2000}
}
Last modified: 17/05/01
Agnes.Cortell@sophia.inria.fr