Accurate extraction of object contour from complex and dynamic traffic images is crucial for intelligent vehicles, and plays an important role for pedestrian protection. Snake Models are widely employed to object contour outomatic extraction. Presented here is a novel approach for pedestrian recognition by combining stereovision with Snake models. Object segmentation based on dense disparity map is used to locate and break up regions which contains potential pedestrians. Furthermore, edge-indexed stereo matching algorithm is employed to obtain the initial edges of the targets of the regions in order to facilitate object contour extraction in the later stage. Snake models are adopted to extract complete contour curves of the targets. Contour factors derived from the contour curves and target elevation are used to verify the targets i.e. pedestrian recognition. To overcome the limitations of Snake models, distance potential models are modified to immunize from noise and to make the model converge into the boundary concavity. The approach presented here is tested on substantial traffic images and the corresponding results prove the efficiency of the approach.