Abstract
This paper investigates the extraction of volleyball players’ skeleton information and provides a deep learning-based solution for recognizing the players’ actions. For this purpose, the convolutional neural network-based approach for recognizing volleyball players’ actions is used. The Lie group skeleton has a large data dimension since it is used to represent the features retrieved from the model. The convolutional neural network is used for feature learning and classification in order to process high-dimensional data, minimize the complexity of the recognition process, and speed up the calculation. This paper uses the Lie group skeleton representation model to extract the geometric feature of the skeleton information in the feature extraction stage and the geometric transformation (rotation and translation) between different limbs to represent the volleyball players’ movements in the feature representation stage. The approach is evaluated using the datasets Florence3D actions, MSR action pairs, and UTKinect action. The average recognition rate of our method is 93.00%, which is higher than that of the existing literature with high attention and reflects better accuracy and robustness.
Highlights
Volleyball players’ action recognition has obtained a lot of attention in recent years, thanks to advances in computer vision, artificial intelligence, and pattern recognition
Feature representation based on optical flow and motion information, spatiotemporal interest points, descriptive feature representation, and static feature representation based on shape are the most common ways. ere are a number of things to consider, including the volleyball players’ high degree of freedom, background confusion, camera movement and zooming, lighting changes, and video noise
The popular classic data acquisition methods are Kinect somatosensory technology and motion capture technology [14,15,16,17,18,19]. e methods of action feature extraction are mostly based on data sources, mainly including (1) feature extraction method based on the RGB colour image and depth image, which mainly extracts the spatial features of volleyball players’ movement and (2) feature extraction method based on bone information, which mainly extracts the position coordinates of bones and joints, spatiotemporal changes, and limb angle, respectively
Summary
Volleyball players’ action recognition has obtained a lot of attention in recent years, thanks to advances in computer vision, artificial intelligence, and pattern recognition. For the development of volleyball players’ action recognition, machine learning algorithm is a popular approach for action recognition. Is research laid the foundation for the development of action recognition of volleyball players based on skeletal limbs. With the advent and application of Kinect and other depth sensors, the authors carried out pioneering work to estimate the joint position of volleyball players from the depth map [3], which promoted the development of research on action recognition of volleyball players based on bone joints. Volleyball players’ action recognition mainly includes two stages of feature extraction and feature classification.
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