Abstract. Many techniques have been proposed for segmenting feature point trajectories tracked through a video sequence into independent motions. It has been found, however, that methods that perform very well in simulations perform very poorly for real video sequences. This paper resolves this mystery by analyzing the geometric structure of the degeneracy of the motion model. This leads to a new segmentation algorithm: a multi-stage unsupervised learning scheme first using the degenerate motion model and then using the general 3-D motion model. We demonstrate by simulated and real video experiments that our method is superior to all existing methods in practical situations.
A higher order scheme is presented for the optimal correction method of Kanatani [5] for triangulation from two views and is compared with the method of Hartley and Sturm [3]. It is pointed out that the epipole is a singularity of the Hartley-Sturm method, while the proposed method has no singularity. Numerical simulation confirms that both compute identical solutions at other points. However, the proposed method is significantly faster.
A very compact algorithm is presented for fitting an ellipse to points in images by maximum likelihood (ML) in the strict sense. Although our algorithm produces the same solution as existing ML-based methods, it is probably the simplest and the smallest of all. By numerical experiments, we show that the strict ML solution practically coincides with the Sampson solution.
Feature point tracking over a video sequence fails when the points go out of the field of view or behind other objects. In this paper, we extend such interrupted tracking by imposing the constraint that under the affine camera model all feature trajectories should be in an affine space. Our method consists of iterations for optimally extending the trajectories and for optimally estimating the affine space, coupled with an outlier removal process. Using real video images, we demonstrate that our method can restore a sufficient number of trajectories for detailed 3-D reconstruction.
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