Model-based Recognition of Human Posture using Single Synthetic Images

Model-based vision has been predominantly concerned with the recognition of single component, rigid objects. This paper describes work attempting to recover the 3D structure of a multi-component, highly articulated object the human body. A major goal of this research has been to go beyond the level of basic object recognition, and attempt to reach a semantic level of description regarding the object's behaviour. Previous research on the recognition of human figures has assumed that the behaviour of the figure in the image is known a priori, for example, "walking", or has made use of motion information derived from image sequences. This research shows that accurate 3D structure can be recovered without such knowledge, and that descriptions of a human figure's behaviour can be obtained in terms of static posture descriptions such as; "sitting", "kneeling", "standing".