In the last few years it has been made clear to the research community that further improvements in classic approaches for solving low level computer vision and image/video understanding tasks, are difficult to obtain. New approaches start evolving, employing knowledge-based processing, though transforming a priori knowledge to low level models and rules is far from being straightforward. In this paper, we examine one of the most popular active contour models, the Snakes, we propose a snake model, modifying basic terms and introducing a model-based one, in order to eliminate basic problems that appear in Snakes and introduce prior shape knowledge in the model. A probabilistic rule-driven utilization of the proposed model is being followed to cope with objects of different shape complexities and motion, different environments, indoor and outdoor, cluttered sequences, cases where background is complex (not smooth) and when moving objects get partially occluded. The proposed method has been tested in a variety of sequences and the experimental results verify its efficiency.