In this paper we present an approach for motion segmenta- tion using independently detected keypoints instead of commonly used tracklets or trajectories. This allows us to establish correspondences over non-consecutive frames, thus we are able to handle multiple object oc- clusions consistently. On a frame-to-frame level, we extend the classical split-and-merge algorithm for fast and precise frame-to-frame motion segmentation. Globally, we cluster multiple frame-to-frame motion seg- mentations of different time scales with an accurate estimation of the number of motions. On the standard benchmarks, our approach performs best in comparison to all algorithms which are able to handle missing data. We further show that it works on benchmark data with more than 98% of the input data missing. Finally, we demonstrate the strength of our approach on a challenging mobile-phone-recorded sequence with multiple objects occluded at the same time.