Abstract
Sports video moving target detection and tracking play an important role in enhancing the popularity of sports and the promotion of sports events. This paper combines the SIFT algorithm to carry out the research of sports video moving target detection and tracking technology, to identify sports features, and to improve the sports feature detection algorithm. Moreover, this paper divides the point cloud data into multiple cube grids under the coordinate system where it is located, and then finds the center of gravity of the data points in each grid, and replaces the coordinates of all points in the grid with the coordinates of the center of gravity. In addition, this paper combines data analysis to verify the algorithm and build a sports video moving target detection system. The experimental research results verify that the sports video target detection and tracking technology based on the SIFT algorithm proposed in this paper has good results.
Highlights
In recent years, sports behavior recognition technology has been increasingly integrated into daily sports video analysis
Spatial Transformation Repetition Rate rtrans. e original point cloud is rotated along the three coordinate axes of x, y, z by π/8, π/4, π/3, π/2, π3π/2, 2π, the key points before and after the rotation are detected, and the repetition rate is calculated. e repetition rate calculated on the 4 data sets is shown in Table 1. e rotation angle-repetition rate curve shown in Figure 4 is drawn based on the average value of rnoise on 4 data sets
Both methods have a certain inhibitory effect on Gaussian noise. e Intrinsic Shape Signatures (ISS) algorithm uses the eigenvalues of the covariance matrix to determine the key points, but it does not use the scale space to further smooth it like 3D-SIFT, so it is more sensitive to noise. e Local Surface Patch (LSP) algorithm and the KPQ algorithm use the combination of principal curvature and Gaussian curvature at the key points to form the shape
Summary
Received 27 December 2021; Revised 16 January 2022; Accepted 21 January 2022; Published 11 February 2022. Sports video moving target detection and tracking play an important role in enhancing the popularity of sports and the promotion of sports events. Is paper combines the SIFT algorithm to carry out the research of sports video moving target detection and tracking technology, to identify sports features, and to improve the sports feature detection algorithm. This paper divides the point cloud data into multiple cube grids under the coordinate system where it is located, and finds the center of gravity of the data points in each grid, and replaces the coordinates of all points in the grid with the coordinates of the center of gravity. This paper combines data analysis to verify the algorithm and build a sports video moving target detection system. E experimental research results verify that the sports video target detection and tracking technology based on the SIFT algorithm proposed in this paper has good results This paper combines data analysis to verify the algorithm and build a sports video moving target detection system. e experimental research results verify that the sports video target detection and tracking technology based on the SIFT algorithm proposed in this paper has good results
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