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We have developed a new contour-based tracking algorithm that uses a sequence of template deformations to model and track generic video objects. We organize the deformations into a hierarchy: globally affine deformations, piecewise (locally) affine deformations, and arbitrary smooth deformations (snakes). This design enables the algorithm to track objects whose pose and shape change in time compared to the template. If the object is not a rigid body, we model the temporal evolution of its shape by updating the entire template after each video frame; otherwise, we only update the pose of the object. Experimental results demonstrate that our method is able to track a variety of video objects, including those undergoing rapid changes. We quantitatively compare our algorithm with its constituent pieces (e.g., the snake algorithm) and show that the complete algorithm can track objects with moving parts for a longer duration than partial versions of the hierarchy. It could be benefited from a higher level algorithm to dynamically adjust the parameters and template deformations to improve the segmentation accuracy further. The hierarchical nature of this algorithm provides a framework that offers a modular approach for the design and enhancement of future object-tracking algorithms.

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Active contours or snakes are an effective edge-based method in segmenting an object of interest. However, the segmented boundary of a moving object in one video frame may lie far from the same moving object in the next frame due to its rapid motion, causing the snake to converge on the wrong edges. To guide the snake toward the appropriate edges, we have added gradient-directional information to the external image force to create a "directional snake." Thus, in minimizing the snake energy, the new method considers both the gradient strength and gradient direction of the image. Experimental results demonstrate that the directional snake can provide a better segmentation than the conventional method in certain situations, e.g., when there are multiple edge candidates in the neighborhood with different directions. The directional snake is significant because it provides a framework to incorporate directional information in digital video segmentation.

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