Video processing and analysis applications are part of Artificial Intelligence. Frequently, silhouettes in video frames lack depth information, especially in case of a single camera. In this work, we utilize a three-dimensional human body model, combined with a calibrated fish-eye camera, to obtain three-dimensional (3D) clues. More specifically, a generic 3D human model in various poses is derived from a novel mathematical formalization of a well-known class of geometric primitives, namely the generalized cylinders, which exhibit advantages over the existing parametric definitions. The use of the fish-eye camera allows the generation of rendered silhouettes, using these 3D models. Moreover, we present a very efficient algorithm for matching that 3D model with a real human figure in order to recognize the posture of a monitored person. Firstly, the silhouette is segmented in each frame and the calculation of the real human position is calculated. Subsequently, an optimization process adjusts the parameters of the 3D human model in an attempt to match the pose (position and orientation relatively to the camera) of real human. The experimental results are promising, since the pose, the trajectory and the orientation of the human can be accurately estimated.