A novel method of parametric active contours with geometric shape prior is presented in this paper. The main idea of the method consists in minimizing an energy function that includes additional information on a shape reference called a prototype (mean shape). Shape prior introduced through a similarity measurement between evolving contour and Procrustes mean shape of desired object. This similarity measurement is Procrustes distance between these two contours that is invariant with respect to similarity transformations (translation, scaling, and rotation). Procrustes mean shape is extracted from a set of sample shapes. We apply k-mean clustering to sample shapes for overcoming non Gaussian distribution of them e.g., multiple views of a 3D object. In this case the final energy function is invariant with respect to changing viewpoint of 3D object. This extra shape knowledge enhances the model robustness to noise, occlusion and complex background. We also use gradient direction information in addition to its magnitude for more robustness to complex background and noise. We obtain promising results for 3D object tracking and segmentation showing the robustness of our method against noise, complex background, similarity transformations, occlusion, and changing viewpoint of 3D object.