A visual tracking algorithm by integrating rigid model and snakes

This paper presents a robust vision algorithm for tracking the boundary of an object with an arbitrary shape by using monocular image sequences. This method consists of a curve registration based optimization technique and a deformable contour model (”snakes”) for the global and the local motion estimations, respectively. By combining techniques, we overcome, among other problems, inaccurate estimate of motion parameters in the curve registration method (which apparently only occur when a rigid or a flexible object is tracked), and the “local position variation” of the deformable contour model, variations which are due to noisy images and/or complex backgrounds. The curve registration method uses an iterative algorithm to find the minimum normal distance between two curves, one before motion and the corresponding curve after it. Snakes overcome the limitation of the curve registration method, which suffers from the inaccuracy of motion models. We also propose an internal force, which increases local robustness of the deformable contour to background noise. By using the refined snakes’ control points, the global update of the previous curve is performed for the re-location of the registered curve. Additionally, we integrate the geometric invariant value of the boundary contour and the curve registration method to solve the occlusion problem in visual tracking. The proposed method is validated through experiments on real images.