Controlling messy errors in virtual reconstruction of random sports image capture points for complex systems

When athletes perform different sports, the angles of joints and the speed of movement are different, which causes the feature capture points to not correspond to the key areas well. This results in forming feature messy correspondence errors, and affecting the accuracy of shape basis calculation. The traditional 3D reconstruction algorithms for sports images are affected by the messy profile, and it is difficult to counter the accuracy of later modeling. Therefore, an error control mechanism in the virtual modernization of random sports image collected points is recommended for the complex systems. In this work, the factorization method is used to establish a 3-D dynamic simulation structure of the human body when it is moving. The stolt transformation is implemented for adjusting the azimuth offset rate of all image capturing regions, such that it cannot generate excessive errors during feature matching. The large messy residual error is used for third-order signal compensation for realizing the 3-D dynamic simulation structure in the humanoid signal image sequence. The observed outcomes shows that this approach improves the authenticity of 3-D dynamic simulation of human signal image classifications. Using 150 images and 915 key points, an authenticity coefficient of 0.86 is achieved by the proposed approach outperforming the other state-of-the-art algorithms.