Video smoke detecting method based on color invariance

A detection method and smoke technology, which are applied in the field of pattern recognition, can solve the problems of lowering the accuracy of detection of moving areas and lowering of smoke detection, and achieve the effects of small calculation amount, improved accuracy and good robustness.

Active Publication Date: 2017-11-14
NANJING UNIV OF SCI & TECH
4 Cites 4 Cited by

AI-Extracted Technical Summary

Problems solved by technology

The above two methods mainly rely on the motion characteristics of the smoke, and rarely use the essential feature of the smoke color, and the extraction of the motion area only relies on sim...
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Abstract

The invention provides a video smoke detecting method based on color invariance. The method comprises the steps of firstly adaptively updating an image sequence background and obtaining a real-time background image; then performing normalization processing on a current image and the background image for obtaining the image with translation invariance and scale zooming invariance; performing motion area detection on the normalized current image and the background image by means of an optical gain, and filtering the motion area without a smoke color by means of an optical characteristic of the smoke; and finally confirming the smoke area of the motion area from the motion area with the smoke color by means of a color invariance descriptor. The video smoke detecting method has advantages of low calculation amount, high real-time performance, high efficiency, and high robustness for illumination change and noise corrosion which commonly occur in an outdoor monitoring video.

Application Domain

Character and pattern recognition

Technology Topic

Color invarianceTranslation invariance +4

Image

  • Video smoke detecting method based on color invariance
  • Video smoke detecting method based on color invariance
  • Video smoke detecting method based on color invariance

Examples

  • Experimental program(1)

Example Embodiment

[0068] Example 1
[0069] We test our method on real smoke videos from Bilkent University. The video used comes from the public video library on the Internet http://signal.ee.bilkent.edu.tr/VisiFire/Demo/SmokeClips. Six groups of video sequences in different environments are used for detection, namely: sWindow.avi, sBehindtheFence.avi, sEmptyR1.avi, sMoky.avi, sWasteBasket.avi, sBtFence2.avi. The videos contain outdoor and indoor environments under varying lighting conditions. The detection effect is as Figure 8 As shown, it can be seen that the method of the present invention has high smoke detection accuracy, stable performance, and effectively eliminates the interference of rigid moving objects (such as people) and similar colors (such as the sky).

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