Название: Human Pose Analysis: Deep Learning Meets Human Kinematics in Video
Автор: Songlin Du, Takeshi Ikenaga
Издательство: Springer
Год: 2025
Страниц: 213
Язык: английский
Формат: pdf (true), epub
Размер: 50.7 MB

This book stands at the intersection of Computer Vision, Artificial Intelligence (AI), and human kinematics, offering a comprehensive exploration of the principles, methodologies, and applications of human pose analysis in video data. It covers two main aspects: human body pose analysis and human head pose analysis. Human body pose analysis involves estimating the position and orientation of major joints and body parts, such as the head, neck, shoulders, elbows, wrists, hips, knees, and ankles, to capture the entire body posture in 2D or 3D space. In contrast, human head pose analysis focuses solely on the head’s orientation, typically estimating the angles of rotation around the yaw, pitch, and roll axes to determine the direction in which a person is looking or tilting their head. The proposed method was implemented by programming in Python under the framework of PyTorch.

With the rapid technological advancements and the proliferation of digital media in the contemporary world, the study of human behavior using computer technologies has taken on new dimensions. Among the most intriguing and consequential areas of research within this field is the analysis of human pose in video. Understanding human behavior in video offers insights into the complexities of human nature, social dynamics, cognitive processes, and human-machine interactions, with wide applications in fields such as security, surveillance, sports analysis, health-care monitoring, and entertainment. It provides a rich source of information for addressing societal challenges, improving human-computer interaction, and enhancing our understanding of ourselves and the world around us.

The book is divided into three parts, each detailing recent research in different areas of pose analysis. The first chapter provides an overview of human body and head pose analysis, including the fundamental principles of kinematic representation, as well as commonly used datasets and evaluation metrics. The first part, consisting of Chapters 2 and 3, delves into 2D human body pose analysis. The second part, spanning Chapters 4 through 7, covers the latest advancements in 3D human body pose estimation, focusing on inferring 3D positions and orientations of body joints from 2D images or videos. The third part, covering Chapters 8 and 9, presents recent studies on 3D human head pose analysis, encompassing both 3D head pose estimation and prediction. The final chapter concludes by summarizing the techniques discussed and outlining future research directions and applications in human body and head pose analysis.