Comparative Analysis of Volleyball Serve Action Based on Human Posture Estimation

Abstract

Serving is one of the most crucial techniques in volleyball. Serving is a method that does not require team interaction and is difficult for the opponent to immediately interfere with. The feature migration module with a fixed offset is suggested in this work. This module can be thought of as a cross-channel dilated convolution approximation of dilated convolution. The reason cross-channel dilated convolution is not worse than standard dilated convolution with few parameters is discussed in this article. An improved random forest model is put forth to address the issue of the human pose estimation system’s high memory consumption when utilizing random forest as the classifier. This model presents the Poisson process and incorporates it with the depth data to create a filter before using Bootstrap sampling. In order to optimize and reconstruct the training dataset, a portion of the feature sample points that do not contribute positively to subsequent classification is removed from the original training dataset. This allows the training dataset to better account for the repeated sampling of the random forest during the sampling process. Resampling has some drawbacks, but they are not very representative. The effectiveness of the optimization model, which significantly lowers the system’s time and space complexity and increases the system’s applicability, is demonstrated by experiments.