Motion Segmentation by Subspace Separation: Model Selection and Reliability Evaluation

Reformulating the Costeira–Kanade algorithm as a pure mathematical theorem, we present a robust segmentation procedure, which we call subspace separation, by incorporating model selection using the geometric AIC. We then study the problem of estimating the number of independent motions using model selection. Finally, we present criteria for evaluating the reliability of individual segmentation results. Again, model selection plays an important role. We confirm the effectiveness of our method by experiments using synthetic and real images.