Robust Contour Tracking Using a Modified Snake Model in Stereo Image Sequences

摘要

In this paper, we present a robust contour tracking method using a modified snake model in stereo image sequences. The main obstacle preventing typical snake-based methods from converging to boundary concavities with gourd shapes is the lack of sufficient energy near the concavities. Moreover, previous methods suffer drawbacks such as high computation cost and inefficiency with cluttered backgrounds. Our proposed method solves the problem utilizing the binormal vector and disparity information. In addition, we apply an optimization scheme on the number of snake points to better describe the object's boundary, and we apply a region similarity energy to handle cluttered backgrounds. The proposed method can successfully define the contour of the object, and can track the contour in complex backgrounds. Performance of the proposed method has been verified with a set of experiments.