This report addresses the problem of human motion tracking from image sequences. The human body is described by an articulated mechanical chain and human body-parts are described by volumetric primitives with curved surfaces. An extremal contour appears in an image whenever a curved surface turns smoothly away from the viewer. We describe a method that relies on a kinematic parameterization of such extremal contours. The apparent motion of these contours in the image plane is a function of both the rigid motion of the surface and the relative position and orientation of the viewer with respect to the curved surface. First, we describe a parameterization of an extremal-contour point, and its associated image velocity, as a function of the motion parameters of the kinematic chain associated with the human body. Second, we introduce the zero-reference kinematic model and we show how it may be used for human-motion modelling. Third, we show how the chamfer-distance may be used to measure the discrepancy between predicted extremal contours and observed image contours; Moreover we show how the chamfer distance can be used as a differentiable multi-valued function and how the tracker based on this distance can be cast in an optimization framework. Fourth, we describe a practical human-body tracker that may use an arbitrary number of cameras. One great methodological and practical advantage of our method is that it relies neither on model-to-image, nor on image-to-image point matches. In practice we model people with 5 kinematic chains, 19 volumetric primitives, and 54 degrees of freedom; We observe silhouettes in images gathered with several synchronized and calibrated cameras. The tracker has been successfully applied to several complex motions gathered at 30 frames/second.