Method for extracting suspected smoke area from dynamic smoke
A smoke and area technology, applied in image enhancement, image analysis, instruments, etc., can solve the problem of inaccurate video smoke detection algorithm, and achieve the effect of accurate extraction and reduced missed detection rate
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specific Embodiment approach 1
[0034] A method for extracting suspected smoke areas in dynamic smoke in this embodiment, the method is implemented through the following steps: figure 1 as shown,
[0035] Step 1, preprocessing the input video image;
[0036] Perform denoising processing on the input video image, and improve the anti-interference ability of the target area by selecting the color space and extracting key frame processing steps;
[0037] Specifically:
[0038] First, input a video image and process it frame by frame;
[0039] After that, the video data set is screened and cropped to obtain the format of the data video file and a video image of uniform size;
[0040] Afterwards, the video image is normalized based on the size of the smoke data set, that is, according to the size of the smoke data set is 32x24, it is normalized into a video file with a size of 320x240. and sent to the recognition model;
[0041] Afterwards, utilize filter to carry out denoising processing to video image; Des...
specific Embodiment approach 2
[0053] The difference from the specific embodiment 1 is that in this embodiment, a method for extracting suspected smoke areas in dynamic smoke, the step of detecting the corners of moving objects described in step 2 is specifically:
[0054] A corner point is a feature point where the gray value of the pixel point changes drastically when moving in the horizontal and vertical directions. Input the preprocessed smoke video image frame by frame, and go through corner detection frame by frame to find corner points:
[0055] exist image 3 , 4 It is obvious that there will be corner points in the rectangular frame area due to smoke in the area. The following methods are used to calculate and judge the corner points of the smoke area in the image:
[0056] Record the image as I(x, y), and the similarity after translating (Δx, Δy) at point (x, y) is:
[0057]
[0058] ω(x,y) is a window centered on point (x,y), that is, a weighting function. For example, the Gaussian weightin...
specific Embodiment approach 3
[0073] The difference from the second specific embodiment is that in this embodiment, a method for extracting a suspected smoke area in dynamic smoke, the step of performing optical flow vector estimation on the corner points of the moving object described in step two is specifically:
[0074] Perform optical flow vector estimation on the corner points detected in the smoke video image, formula (4) to formula (11), can obtain the corner point optical flow vector diagram, see Figure 5 as shown,
[0075] The optical flow is defined as the instantaneous speed of pixel motion on the image plane, and the distance between the previous frame and the current frame is found according to the change of pixels in the image sequence in the time domain and the correlation between adjacent frames. The corresponding relationship exists, so as to calculate the motion information of the object between adjacent frames;
[0076] According to the degree of density of the vector in the optical fl...
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