Springer Human Pose Analysis: Deep Learning Meets Human Kinematics in Video

This book stands at the intersection of computer vision, artificial intelligence, 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 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.