1. Wavelet Based Real-time Smoke Detection In Video
This paper was published in the journal Signal Processing: Image Communication, EURASIP, 2005

2. Goal of the method proposed
Early detection of fire before it spreads around.
Why?
Conventional fire detectors have limitations:
They can’t detect on time the smoke or fire in large rooms.
They can’t operate in open spaces.
How?
Use of ordinary video to detect smoke.

3. Approach of the method proposed
Edges of images start losing their sharpness when smoke is present and thus high frequency content of the image is decreasing.
Background of the scene is estimated and spatial wavelet transforms are used to monitor the change of the high frequency content.
Change in pixels intensity values as well as analysis of the flicker and convexity of the smoke regions are used to build the method proposed.

4. Fourier Analysis vs Wavelet Analysis
Fourier Analysis breaks down a signal into constituent sinusoids of different frequencies. It is a mathematical technique for transforming our view of the signal from a time-based one to a frequency-based one.

5. Fourier Analysis vs Wavelet Analysis
Disadvantage of Fourier Analysis: When looking at a Fourier Transform of a signal, it is impossible to tell, when a particular event took place.
Wavelet Analysis is capable of revealing aspects of data, like trends, breakdown points and discontinuities in higher derivatives.

6. Smoke Detection Algorithm
Step 1: Determining the moving pixels or regions in the current frame of a video.
Step 2: Checking the decrease in high frequency content of the edges in these regions.
If the edges lose their sharpness, then:
Step 3: The decrease in U and V channels of them are checked.
Step 4: Flicker analysis is carried out using temporal wavelet transform.
Step 5: Shape of the moving region is checked for convexity.

7. Background Estimation & Wavelet Subimages
Composite image containing high-frequency information at a given scale:

8. Wavelet Subimages
Wavelet subimages are used to derive information about the location of smoke. They are divided into small blocks of size (Κ1, Κ2) and the energy e(l1, l2) is computed:
Wavelet subimages contain also the edge information of the original image. Slow fading of a wavelet extrema is clue for smoke detection. Two thresholds are set for checking the decrease in visibility:

9. Other Parameters That Are Considered
Color information
As the smoke gets thicker, the chrominance values U and V of the candidate region in the current frame become smaller than the ones in the background image.
Flicker of smoke
The candidate regions are checked whether they continuously appear and disappear over time.
Convexity
Boundaries of the moving regions are checked for their convexity along equally spaced vertical and horizontal lines.

10. Applications Relevant to the Paper
July 2018, Greece
November 2018, California

11. Applications Relevant to the Paper
Navigation in visually degraded conditions

12. Thank you! Questions?
Maria Tsiourva