Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

Abstract

Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and autonomous driving. Improving the accuracy of pose estimation has significant implications for the advancement of computer vision. This paper addresses the limitations of single-branch networks in pose estimation. It presents a top-down single-target pose estimation approach based on multi-branch self-calibrating networks combined with graph convolutional neural networks. The study focuses on two aspects: human body detection and human body pose estimation. The human body detection is for athletes appearing in sports competitions, followed by human body pose estimation, which is divided into two methods: coordinate regression-based and heatmap test-based. To improve the accuracy of the heatmap test, the high-resolution feature map output from HRNet is used for deconvolution to improve the accuracy of single-target pose estimation recognition.

Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method

“…In practical applications, the data often do not entirely obey the same distribution for various reasons, such as different times and regions (Naik et al, 2023). This requires transfer learning techniques to apply the learned knowledge from the source domain to the target domain (Duan et al, 2023). A complex and valuable practical problem is that there is a difference in the distribution of data in the source and target domains, and there is no labeled data in the target domain (Zhou et al, 2023).…”

MCP: Multi-Chicken Pose Estimation Based on Transfer Learning

“…Human pose estimation is a computer vision technology that detects and recognizes human body joint positions and their relationships using algorithms to reconstruct human postures [ 12 , 13 , 14 ]. It is widely used in action recognition [ 15 ], virtual reality [ 16 ], behavior analysis [ 17 ], and sports monitoring [ 18 ]. It typically involves detecting joints and constructing the human skeleton using 2D images or 3D data from cameras.…”

Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network

“…In recent years, many studies have been conducted that analyzed the performance of individual players in sports domains [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. While there are many studies that focus on motion analysis, most require athletes to wear sensors to acquire their posture data; however, this method easily introduces noise due to friction during motions [ 1 , 2 , 8 ].…”

“…While there are many studies that focus on motion analysis, most require athletes to wear sensors to acquire their posture data; however, this method easily introduces noise due to friction during motions [ 1 , 2 , 8 ]. Therefore, the video-based contactless approach, which aims to acquire whole posture data using a simple camera without placing sensors on body parts, has made it easier and more convenient for scholars to analyze a variety of postures in many exciting sports domains [ 3 , 4 , 6 , 7 ].…”

“…The dissimilarity matrix among all player pairs using 2D input graph-embedding model ×107 . The green numbers indicate the most similar pairing results, while the red numbers represent the least similar pairing results.…”