|Institution:||KTH Royal Institute of Technology|
|Keywords:||Natural Sciences; Computer and Information Science; Computer Science; Naturvetenskap; Data- och informationsvetenskap; Datavetenskap (datalogi)|
|Full text PDF:||http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153943|
Motion segmentation of RGB-D videos can be a first step towards object reconstruction in dynamic scenes. The objective in this thesis is to end an ecient motion segmentation method that can deal with a moving camera. To this end, we adopt a feature-based approach where keypoints in the images are tracked over time. The variation in the observed pairwise 3-d distances is used to determine which of the points move similarly. We then employ spectral clusteringto group trajectories into clusters with similar motion, thereby obtaining a sparse segmentation of the dynamic objectsin the scene. The results on twenty scenes from real world datasets and simulations show that while the method needs more sophistication to segment all of them, several dynamic scenes have been successfully segmented at a processing speed of multiple frames per second.