Model-Based Pose Estimation

Abstract

Model-based pose estimation algorithms aim at recovering human motion from one or more camera views and a 3D model representation of the human body. The model pose is usually parameterized with a kinematic chain and thereby the pose is represented by a vector of joint angles. The majority of algorithms are based on minimizing an error function that measures how well the 3D model fits the image. This category of algorithms usually has two main stages, namely defining the model and fitting the model to image observations. In the first section, the reader is introduced to the different kinematic parametrization of human motion. In the second section, the most commonly used representations of the human shape are described. The third section is dedicated to the description of different error functions proposed in the literature and to common optimization techniques used for human pose estimation. Specifically, local optimization and particle-based optimization and filtering are discussed and compared. The chapter concludes with a discussion of the state-of-the-art in model-based pose estimation, current limitations and future directions.