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Research on the Application of Computer Vision Technology in Sports Mechanics Analysis
Abstract
With the continuous development of computer vision technology and related hardware devices, motion mechanics analysis is gradually being applied in sports training. However, due to the complex characteristics of motion mechanics and irregular movements, motion mechanics analysis still has limitations in practical application scenarios. This article constructs a multi convolutional 3D CNN model that combines BN algorithm, dropout technique, and spatial pyramid pooling technique. Different features are used as inputs for 3D CNN models tested on video datasets. The experimental results show that combining the “BM+OFM+three FDF” features as model inputs can achieve high recognition accuracy. Therefore, the 3D CNN model constructed in this article can effectively improve the accuracy of motion mechanics recognition and has good application value.
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