Corner Tracking through Multiple Hypotheses Matching

F. Mokhtarian and F. Mohanna (UK)


corner detection, curvature scale space, canny edge detector, corner matching, multiple hypotheses


This paper presents a corner tracking algorithm which maintains multiple hypotheses while tracking corners. To extract corners from each frame of video sequences, the multi-scale enhanced CSS1 corner detector is applied. In the matching stage two-frame correspondence using multiple matching combined with three-frame based monitoring is employed. Considering multiple hypotheses helps to ensure that if the corner detection is not completely robust, we still have a chance to find as close as possible a match or multiple matches for tracked corners. False matches are removed using a distance criterion based on a reference point. The value of threshold in the distance criterion is constant and does not need any adjustment. Since proposed tracker does not make any assumptions or use any special motion models in tracking feature points, it is more practical and efficient for unconstrained and non-smooth motions. Experiments have been carried out on a wide range of real video sequences. All experiments confirm that the proposed corner tracker performs reliably and more independently of corner detector robustness.

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