  We present a novel method for motion segmentation called LAAV (Locally Affine
Atom Voting). Our model's main novelty is using sets of features to segment
motion for all features in the scene. LAAV acts as a pre-processing pipeline
stage for features in the image, followed by a fine-tuned version of the
state-of-the-art Random Voting (RV) method. Unlike standard approaches, LAAV
segments motion using feature-set affinities instead of pair-wise affinities
between all features; therefore, it significantly simplifies complex scenarios
and reduces the computational cost without a loss of accuracy. We describe how
the challenges encountered by using previously suggested approaches are
addressed using our model. We then compare our algorithm with several
state-of-the-art methods. Experiments shows that our approach achieves the most
accurate motion segmentation results and, in the presence of measurement noise,
achieves comparable results to the other algorithms.
