Motion segmentation for 2D videos is usually based on tracked 2D point motions, obtained for a sequence of frames. However, the 3D real world motion consistency is easily lost in the process, due to projection from 3D space to the 2D image plane. Several approaches have been proposed in the literature to recover 3D motion consistency from 2D point motions. To further improve on this, we here propose a new criterion and associated technique, which can be used to determine whether a group of points show 2D motions consistent with joint 3D motion. It is also applicable for estimating the 3D motion information content. We demonstrate that the proposed criterion can be applied to improve segmentation results in two ways: finding the misclassified points in a group, and assigning unclassified points to the correct group. Experiments with synthetic data and different noise levels, and with real data taken from a benchmark, give insight in the performance of the algorithm under various conditions.
|Title of host publication||Computer Analysis of Images and Patterns|
|Editors||Michael Felsberg, Anders Heyden, Norbert Krüger|
|Place of Publication||Cham|
|Number of pages||13|
|Publication status||Published - 2017|
|Series||Lecture Notes in Computer Science|