Wide-angle multi-view video, which provides viewers, with a realistic experience has received increasing attention in recent years. Users want to watch such videos interactively, switching viewpoints freely, but without the burdens of consecutive viewpoint selection or complex operation. Viewing systems should therefore satisfy these conflicting needs simultaneously. In this paper, we take the novel approach of confronting multi-view videos as a cooperative work. We also introduce a human-machine cooperative viewing system for wide-angle multi-view videos exploiting target-centered viewing. Our system consists of a manual viewpoint selection function and an automatic viewpoint selection function based on our concept.
We propose a Mean-Shift based feature point tracking method that can track feature points with high accuracy even when they move over a long distance or a wide range on an image. Our method selects an initial value of MeanShift from a wide area by a corresponding point search based on the Kalman filter when the image appearance significantly changes. The corresponding point search responds to the rapid change of the feature points because it corresponds with those detected from sequential images. We used a movement prediction of the tracking point by Kalman filter to reduce the correspondence failure. Mean-Shift search tracks the feature points accurately in a narrow range using the corresponding point as initial value. We evaluated our method by tracking feature points of synthetic image sequences that simulate a movement of the tracking target on the image. The proposed method showed smaller tracking error than both Mean-Shift search and a conventional corresponding feature point search.
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