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Camera calibration is the most important aspect of computer vision research. To address the issue of insufficient precision, therefore, a high precision calibration algorithm for binocular stereo vision camera using deep reinforcement learning is proposed. Firstly, a binocular stereo camera model is established. Camera calibration is mainly divided into internal and external parameter calibration. Secondly, the internal parameter calibration is completed by solving the antihidden point of the camera light center and the camera distortion value of the camera plane. The deep learning fitting value function is used based on the internal parameters. The target network is established to adjust the parameters of the value function, and the convergence of the value function is calculated to optimize reinforcement learning. The deep reinforcement learning fitting structure is built, the camera data is entered, and the external parameter calibration is finished by continuous updating and convergence. Finally, the high precision calibration of the binocular stereo vision camera is completed. The results show that the calibration error of the proposed algorithm under different sizes of checkerboard calibration board test is only 0.36% and 0.35%, respectively, the calibration accuracy is high, the value function converges quickly, and the parameter calculation accuracy is high, the overall time consumption of the proposed algorithm is short, and the calibration results have strong stability.

This article uses the gray system theory of nonlinear mathematical equations to predict the best results of China's track and field events in the 2021 Olympic Games. And compare the above prediction information with the track and field performance of the 2021 Olympic Games. The article uses this to guide the teaching of Chinese track and field sports and analyze the relevant factors affecting Chinese track and field training.

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