Abstract: Active contour model (ACM) incorporating with shape prior (Hybrid driving ACM, HACM) can solve many practical problems, such as occlusion, local deformation, similarity transformation, and is used extensively in image segmentation, contour extraction and so on. Based on the ACM, the paper reviews the contour extraction methods with the shape prior information from two aspects: shape prior knowledge extraction and the corresponding incorporation methods. Firstly, the shape knowledge extraction methods based on invariant moment descriptor, spline function, and level set function are studied. These methods are used for the contour-information extraction of the rigid and non-rigid targets. The information has a certain generalization, so that it can express some contour out of the sample set. Secondly, aiming at the single target contour extraction problem, some global incorporating methods between the prior and primary ACM are investigated in terms of distance function; in addition, a local incorporation method based on label function that can solve the single-knowledge but multi-objective problem is introduced. Finally, the hybrid driving models are summarized and many research directions for further research are suggested.