Real-time foreground extraction method and device based on monocular platform
A foreground extraction, single-purpose technology, applied in the field of image processing, can solve the problems of high hardware requirements, low efficiency and instability, long processing time, etc., to achieve the effect of reducing the amount of algorithm calculation and preventing background learning errors
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Embodiment 1
[0068] like figure 1 As shown, the present embodiment of the present invention provides a real-time foreground extraction method based on a monocular platform, comprising the following steps:
[0069] S110 acquires a monocular video frame sequence image;
[0070] S120 shrinks the video frame sequence image to obtain a reduced size sequence image; by obtaining frame image sequence data and reducing the image to a fixed size, the main calculation amount of the algorithm is controlled.
[0071] S130 extracting a target foreground image according to the reduced-size sequence image;
[0072] S140 performing vector edge enlargement processing on the target foreground image to obtain the foreground vector edge of the original resolution;
[0073] S150 performs anti-aliasing processing on the edge of the foreground vector in the original resolution;
[0074] S160 fills the inside of the foreground vector edge with the foreground color, fills the outside of the foreground vector edg...
Embodiment 2
[0108] Figure 5 Shows the general flow of the real-time foreground extraction algorithm based on the ARM platform according to the embodiment of the present invention, Image 6 The main algorithm modules included in the system are shown, and the specific steps are as follows:
[0109] Step 1: Image reduction, see Figure 5 , Image 6 S1 in. Scale each frame image to a fixed size by obtaining frame image sequence data. This can ensure that the amount of data is basically constant, and the calculation amount of all subsequent operations is guaranteed to be fixed.
[0110] Step 2: Background learning, see Figure 5 , Image 6 S2 in. By obtaining frame image sequence data, counting the probability p, mean value μ and variance σ of each pixel value at each position, using p, μ and σ as a model, and learning multiple models as the background statistical model of the position. A single model refers to the background model of each position in the image, where the model includ...
Embodiment 3
[0161] like Figure 9 As shown, based on the real-time foreground extraction method in the above embodiment, another aspect of the present invention also provides a real-time foreground extraction device 100 based on a monocular platform, including:
[0162] The video frame acquiring unit 110 is configured to acquire a monocular video frame sequence image;
[0163] The image reduction unit 120 is configured to perform reduction processing on the video frame sequence image to obtain a reduced scale sequence image;
[0164] The foreground extraction unit 130 is configured to extract a target foreground image according to the reduced-size sequence image;
[0165] The edge magnification unit 140 is configured to perform vector edge magnification processing on the target foreground image to obtain the foreground vector edge of the original resolution;
[0166] The anti-aliasing unit 150 is configured to perform anti-aliasing processing on the edge of the foreground vector of the ...
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