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Fire Detection for Early Fire Alarm Based on Optical Flow Video Processing

Fire Detection for Early Fire Alarm Based on Optical Flow Video Processing. Suchet Rinsurongkawong1, Mongkol Ekpanyapong , and Matthew N. Dailey Mechatronics , suchet.rinsurongkawong@ait.ac.th Microelectronics and Embedded systems, mongkol@ait.ac.th

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Fire Detection for Early Fire Alarm Based on Optical Flow Video Processing

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  1. Fire Detection for Early Fire Alarm Based onOptical Flow Video Processing Suchet Rinsurongkawong1, MongkolEkpanyapong, and Matthew N. Dailey Mechatronics, suchet.rinsurongkawong@ait.ac.th Microelectronics and Embedded systems, mongkol@ait.ac.th Computer Science and Information Management, mdailey@ait.ac.th Asian Institute of Technology, Pathumthani, Thailand

  2. Outline • Introduction • Methods • Experience result • Future work

  3. Introduction • Fire has always threatened properties and peoples’ lives. • Most conventional fire detection technologies are based on particle sampling, temperature sampling, and smoke analysis,butfire detection systems using these technologies have limited effectiveness due to high false alarm rates. • Because of the rapid developments in digital camera technologyand computer vision system, there are many fire detection technologies which are introduced based on image processing.

  4. Moving region detection • Background subtraction: • Be assumed to be a moving pixel if:

  5. Chromatic features(1/3) • The color of fire always appears in red-yellow range.

  6. Chromatic features(2/3) • To solve from a fire-like color.

  7. Chromatic features(3/3) • Besides, when the fire is in dark background environment without other background illumination, the fire will be the main light source. From this reason, the fire may display in a whole white color in an image. Thus, the intensity should be over threshold intensity IT .

  8. Growth rate analysis • The growth rate rule can be deduced as: • Where Gidenotes quantities of the current frame to the n thframe. • If the result is more than a reference Gr from the first detected frame, the moving object will be considered as a real flame.

  9. Turbulent fire plumes

  10. Turbulent fire plumes • The frequency shows the cycle times of eddies effect per 1 second. • Where f denotes a vortex shedding frequency in Hz for a fire of diameter D in meters.

  11. Lucas-kanade optical flow pyramid • The algorithm of LK is based on 3 assumptions. 1. “Brightness constancy” 2. “Temporal persistence” 3. “Spatial coherence”

  12. Flow rate analysis(1/3) • From the previous step, the LK optical flow can extract the motion velocity vector from each feature point. • Where pand qdenote the starting and the endingpoint of each feature point respectively. n refers to the number of feature points.

  13. Flow rate analysis(2/3) • The average flow rate of the first time of optical flow analysis is calculated as follow: • Where Fadenotes the average flow rate of the first detected time for optical flow analysis. This first average flow rate will be used as a reference value for next n time calculation.

  14. Flow rate analysis(3/3) • variation of flow rate: • Where Fvis the average flow rate from n time calculation,wewill called it “variation of flow rate”. Due to the turbulent of flame, the variation flow rate of fire will give a significant value more than other moving objects.

  15. Expermental result • Find the flow rate threshold value

  16. Method1 & method2

  17. Result from method1

  18. Conclusion and future • In dynamic analysis, the combination of growth rate and Lucas-Kanade optical flow can extract the motion feature of fire, so this method can easily distinguish the disturbances which having the same color distribution as fire. • In the future, the neural network will be applied to train the raising parameters composed of fire-pixels extracted at timeintervalfur increasing the reliability of fire-alarming. The use of neural networks, the statistical values must have highly enough in the training process.

  19. Thanks for your attention!

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