Features in the RGB Color Space

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𝑅 + 𝛼 = 𝐺 + 𝛼 = 𝐵 + 𝛼 Rule2:

𝐿₁≤ 𝐼 ≤ 𝐿₂ (3-16)

Rule3:

𝐷₁ ≤ 𝐼 ≤ 𝐷₂ If (rule1) AND [(rule2) OR (rule3)] = TRUE

Then smoke pixel Else

not smoke pixel

The value of 𝛼 is 15~20, 𝐿₁=150, 𝐿₂=220, 𝐷₁=80, 𝐷₂=150. When the smoke is light gray, rule 2 is used, and when the smoke is dark gray, rule 3 is used.

This theory has a better recognition effect on smoke, but in outdoor smoke videos, due to the long shooting distance and low camera pixels, the image is blurred. In actual situations, non-smoke areas will also appear the color is close to the characteristics of the grayscale image, that is, the values of the three components of R, G, and B are close. This thesis did the following experiment to compare the characteristics of the smoke area and non-smoke area in the RGB color space in the outdoor fire smoke videos.

The above four groups of images are smoke images (Figure 3-6 (1)), non-smoke images in close-up (Figure 3-6 (2)), outdoor non-smoke images (Figure 3-6 (3)), and outdoor non-smoke images (Figure 3-6 (3)). The images after the smoke appears in the smoke area (Figure 3-6 (4)). The second image of each group is the statistics of the number of pixels in the R, G, and B color spaces of the first image. The horizontal axis represents all the pixels from the upper left corner to the lower right corner of the image. The vertical axis represents the size of the pixel value. The red, green, and blue lines respectively represent the three channels of each pixel in the image. The third image in each group is the representation of pixels in the RGB three- dimensional space. Through the analysis of the above chart, the following conclusions can be drawn.

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(1)

(2)

(3)

(4)

Figure 3-6: Four comparison images and the two-dimensional and three-dimensional images of the R, G, B components of each images.

The above four groups of images are smoke images (Figure 3-6 (1)), non-smoke images in close-up (Figure 3-6 (2)), outdoor non-smoke images (Figure 3-6 (3)), and the image after the smoke appears in the smoke

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area (Figure 3-6 (4)). The second image of each group is the statistics of the number of pixels in the R, G, and B color spaces of the first image, the horizontal axis represents all the pixels from the upper left corner to the lower right corner of the image, the vertical axis represents the size of the pixel value, the red, green, and blue lines respectively represent the three channels of each pixel in the image. The third image in each group is the representation of pixels in the RGB three-dimensional space. Through the analysis of the above chart, the following conclusions can be drawn.

1) For the smoke images (Figure 3-6(1)), from the RGB two-dimensional image, the spacing between the three curves is basically the same, and the difference is not very large. From the RGB three-dimensional image, the pixels are evenly distributed the non-smoke images (Figure 3-6(2)) in the close-range position is centered on the diagonal, and the non-smoke images taken at a closer distance. The color of the image is better and clearer. There are three in the RGB two-dimensional image. The interval of the curve is relatively uneven, and the distribution of the pixels in the RGB three-dimensional image is not concentrated near the diagonal. In summary, the characteristics of smoke and fog area conform to the rules of literature [55].

2) For outdoor non-smoke images (Figure 3-6 (3)), in the RGB two-dimensional image, the spacing between the three bars is not uniform. From the RGB three-dimensional image, can get that the pixels are evenly distributed at the diagonal center. Indicating that the image is close to a grayscale image. But the difference between the three RGB channels is not consistent, that is, it does not satisfy the formula 𝑅 + 𝛼 = 𝐺 + 𝛼 = 𝐵 + 𝛼.

3) The images after the appearance of smoke in the outdoor non-smoke area (Figure 3-6 (4)), the RGB two- dimensional image and the RGB three-dimensional image show almost the same characteristics as the outdoor non-smoke images (Figure 3-6 (3)).

In summary, the RGB color space is not obvious for outdoor smoke characteristics, so the method of finding the characteristics of the smoke area in the RGB color space is abandoned. However, in the HSV color space, H represents the hue, which reflects the color of the image, S represents the saturation, which reflects the vividness of the image color, and V represents the value, which reflects the brightness of the image.

Because of the appearance of smoke will make the image blurry, and the smoke area is generally white, it is reflected in the HSV color model that the saturation S is relatively low, and the value V will be relatively increased before and after the smoke appear. Reference [58] proposes that smoke will reduce the saturation of the background. Considering that the smoke cannot cover the background when the smoke is thin, the translucent characteristic of the smoke has this effect, but when the smoke is thick, it can completely cover the background area, so in this thesis chooses the smoke characteristics of is mainly for the color characteristics of the smoke, rather than the effect of the smoke on the background.

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