Abstract
The existing basketball training shooting direction correction method has the problems of low correction accuracy and poor self-adaptability, and proposes a basketball training shooting direction correction method based on visual perception. A visual localisation algorithm for tracking feature points of object targets is used as the basis for the process of visual robot localisation and its effects, from camera calibration, template matching, background modelling and foreground target separation to feature point extraction, motion estimation, and Kalman filtering. An in-depth understanding and analysis of the traditional corner point detection algorithm is presented, on the basis of which improvements are proposed. An accurate tracking method based on improved Harris corner point extraction is introduced, which builds on the traditional Harris feature point detection by using the changing relationship between the gradient of the grey value of the pixels near the corner point, using simple operations and analysis to exclude some pseudocorner points and noncorner points, and further processing the retained points to derive the correct feature points. The code of this algorithm is written to finally achieve its detection effect, and compared with the traditional algorithm, it is concluded that this algorithm can then extract more accurate corner points in a shorter time, which lays the foundation for the next step of accurate basketball tracking, reflecting the practicality of this algorithm.
Highlights
In the context of increasingly sophisticated computer vision and image processing technologies, machine vision is used to recognise images, analyse the video parameters of captured sports images, and feed back into human-computer interaction systems and expert systems to achieve guidance in sports training
In order to adapt the system to different working environments, two distinct image processing algorithms, smoothing and sharpening, have been chosen for this design. e image sharpening algorithm takes into account the fact that the positioning system does not need to focus too much on the details of the acquisition screen, but more on obtaining the contour information of the scene in order to extract the correct corner points; the Sobel sharpening operator is chosen as the edge detection algorithm. e image smoothing process is based on the general median filtering algorithm, which is improved according to the field-programmable gate array (FPGA) processing characteristics, in order to better adapt to the FPGA design requirements and reflect the real-time
In practice, the corner points extracted by the detection methods used are generally feature points that represent the target features and are not always just “corner points.” e Harris feature point detection algorithm was obtained by improving the Moravec algorithm
Summary
In the context of increasingly sophisticated computer vision and image processing technologies, machine vision is used to recognise images, analyse the video parameters of captured sports images, and feed back into human-computer interaction systems and expert systems to achieve guidance in sports training. According to this idea, the basketball training shooting angle correction method is studied, the shooting angle information feature quantity of basketball training is extracted, the basketball training shooting angle parameters and training movement characteristics are analysed, and the correction improvement of basketball training movements is guided. Erefore, this article will focus on the standardisation of basketball shooting direction based on visual localisation, analyse the traditional implementation equipment and algorithms, and design a visual localisation system with more real-time effect and accurate effect
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