Abstract
The efficiency of basketball shooting training has always been an urgent problem to be solved in basketball sports. Applying computer virtual image technology to basketball sports training is the key to improve basketball shooting ability and improve the effect of basketball sports training. Therefore, based on the design of basketball shooting automatic recognition system based on the background difference method, this study puts forward the specific application of computer virtual image technology in modern sports training. By analyzing the techniques of image denoising, image detection, and image calibration, a goal detection algorithm for modern sports basketball shooting training is designed. Firstly, the camera is used for image capture, the RGB image is converted into gray image, and the median filter is used to suppress the noise in the image. Then, the background difference method is used to detect the moving region, and the background modeling is combined with the mean method. After obtaining the background reference model, the image is differentiated, the gray image after image difference is binarized, and then the binary image is postprocessed by morphological middle closure operation. Finally, the image calibration technology is used to extract the basketball feature information. Through the region segmentation algorithm, the basketball shooting part is segmented and judged, so as to realize the basketball shooting training goal detection. The experimental results show that the proposed method has a good effect on basketball shooting training goal detection and can effectively improve the detection accuracy and efficiency.
Highlights
Assuming that the background frame image used in the background difference method is static, that is, the background frame will not change with the number of image frames, b(i, j) is used to represent the background image, μ(i, j, l) is defined as the image sequence, and (i, j) in the image sequence is set as the position coordinate of the image
L is the number of frames in the image sequence, and the gray value of the selected image in the image sequence is used to make the difference with the gray value in the background image, and a difference image μd(i, j, l) moving target is expressed as μd(i, j, l) μ(i, j, l) − b(i, j)
Basketball feature information extraction environment of the scene is relatively simple and the influence of other objective factors is small, this study adopts the mean value method for background modeling. e multiframe images captured by the camera in a period of time are accumulated, and the accumulated value is divided by the number of captured frames, and the average value is obtained, and the obtained average value is used as the background reference model. e mathematical expression is expressed as
Summary
Because the noise in different images has differences in characteristics and spectral distribution, different denoising methods are formed. e mean filtering method and the median filtering method are the two most typical denoising methods in the spatial domain.
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