Track and Field Head Posture Error Correction System Based on Deep Reinforcement Learning

The problem that track and field athletes generally have non-standard postures in their playing actions, a track and field head posture error correction system based on deep reinforcement learning is designed. By optimizing the system configuration, improving the recognition accuracy, using deep reinforcement learning technology to obtain 3D deep dynamic image data of track and field sports, converting the data into quaternion format, storing the data file in VBH format, and shaping the data through deep reinforcement learning technology a dynamic three-dimensional model is used to judge whether the track and field posture is standard using the Euclidean distance comparison method. Using the powerful learning ability of deep reinforcement learning, a series of non-linear operations are performed on the input face image, the abstract features in the image are extracted layer by layer, and then the extracted features are used for classification and recognition and error correction. Finally, through actual research, the standardization of the track and field head attitude error correction system based on deep reinforcement learning is proved. The experimental results show that this method effectively improves the accuracy of attitude estimation.