Dynamic Occluding Contours: A New External-Energy Term for Snakes

Abstract

Dynamic contours, or snakes, provide an effective method for tracking complex moving objects for segmentation and recognition tasks, but have difficulty tracking occluding boundaries on cluttered backgrounds. To compensate for this shortcoming, dynamic contours often rely on detailed object-shape or motion models to distinguish between the boundary of the tracked object and other boundaries in the background. In this paper we present a complementary approach to detailed object models: We improve the discriminative power of the local image measurements that drive the tracking process. We describe a new, robust external-energy term for dynamic contours that can track occluding boundaries without detailed object models. We show how our image model improves tracking in cluttered scenes, and describe how a fine-grained image-segmentation mask is created directly from the local image measurements used for tracking.