Robust corner tracking for unconstrained motions

The paper presents a robust corner tracking method for unconstrained motions. The aim is to track corners in video sequences and identify best correspondences under any transformation, perspective distortion and intensity changes. To extract corners from each video frame, the multi-scale enhanced CSS (curvature scale space) corner detector is applied. In the matching stage, a new two-frame correspondence algorithm using multiple matches is employed. Experiments have been carried out on a wide range of real video sequences depicting translation, scaling, rotation and affine transformation, including non-smooth motions with different lighting and different camera motions. All the results were compared to the results of the popular Tommasini feature tracking algorithm (Tommasini, T. et al., IEEE Conf. on Computer Vision and Pattern Recognition, 1998). Experiments and comparison of the results confirm that the proposed corner tracker is more efficient, and more robust for unconstrained and non-smooth motions. These properties make it specially useful for video database retrieval and multimedia applications.