CONTOUR BASED SMOKE DETECTION IN VIDEO USING WAVELETS
B. Ugur Toreyin, Yigithan Dedeoglu, and A. Enis Cetin
Bilkent University
06800, Bilkent, Ankara, Turkey
{bugur, yigithan, cetin}@bilkent.edu.tr
ABSTRACT
This paper proposes a novel method to detect smoke in video.
It is assumed the camera monitoring the scene is stationary.
The smoke is semi-transparent at the early stages of a fire.
Therefore edges present in image frames start loosing their
sharpness and this leads to a decrease in the high frequency
content of the image. The background of the scene is esti-
mated and decrease of high frequency energy of the scene
is monitored using the spatial wavelet transforms of the cur-
rent and the background images. Edges of the scene pro-
duce local extrema in the wavelet domain and a decrease in
the energy content of these edges is an important indicator
of smoke in the viewing range of the camera. Moreover,
scene becomes grayish when there is smoke and this leads
to a decrease in chrominance values of pixels. Periodic be-
havior in smoke boundaries is also analyzed using a Hidden
Markov model (HMM) mimicking the temporal behavior of
the smoke. In addition, boundary of smoke regions are rep-
resented in wavelet domain and high frequency nature of the
boundaries of smoke regions is also used as a clue to model
the smoke flicker. All these clues are combined to reach a
final decision.
1. INTRODUCTION
In this paper, we present an automatic real-time smoke de-
tection method in video. Conventional point smoke and fire
detectors typically detect the presence of certain particles gen-
erated by smoke and fire by ionization or photometry. An im-
portant weakness of point detectors is that in large rooms, it
may take a long time for smoke particles to reach a detector
and they cannot be operated in open spaces.
The main importance of using ordinary video in fire de-
tection is the ability to serve large and open spaces. Cur-
rent fire detection algorithms are based on the use and anal-
ysis of color and motion information i