Contour based smoke detection in video using wavelets

B. Ugur Toreyin*, Yigithan Dedeoglu, A. Enis Cetin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

150 Citations (Scopus)


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 estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Edges of the scene produce 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 behavior 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 represented 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.

Original languageEnglish
JournalEuropean Signal Processing Conference
Publication statusPublished - 2006
Externally publishedYes
Event14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy
Duration: 4 Sept 20068 Sept 2006


Dive into the research topics of 'Contour based smoke detection in video using wavelets'. Together they form a unique fingerprint.

Cite this