Non-linear statistical models for the 3d reconstruction of human pose and motion from monocular image sequences

摘要

This paper presents a model based approach to human body tracking in which the 2D silhouette of a moving human and the corresponding 3D skeletal structure are encapsulated within a non-linear point distribution model. This statistical model allows a direct mapping to be achieved between the external boundary of a human and the anatomical position. It is shown how this information, along with the position of landmark features such as the hands and head can be used to reconstruct information about the pose and structure of the human body form a monocular view of a sense.