text: 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.
  @cite_31: In this paper, we propose a novel rigid motion segmentation algorithm called randomized voting (RV). This algorithm is based on epipolar geometry, and computes a score using the distance between the feature point and the corresponding epipolar line. This score is accumulated and utilized for final grouping. Our algorithm basically deals with two frames, so it is also applicable to the two-view motion segmentation problem. For evaluation of our algorithm, Hopkins 155 dataset, which is a representative test set for rigid motion segmentation, is adopted, it consists of two and three rigid motions. Our algorithm has provided the most accurate motion segmentation results among all of the state-of-the-art algorithms. The average error rate is 0.77 . In addition, when there is measurement noise, our algorithm is comparable with other state-of-the-art algorithms.
summary: Random Voting (RV) @cite_31 , which is considered as the leading geometric method for motion segmentation partly because of its robustness to noise, has shown particularly successful results with a low computational cost. The algorithm, based on epipolar geometry, is an iterative process of randomized feature selection between two frames, estimating a fundamental matrix from the selected features and vote scores for the rest of the remaining features to be associated with a certain motion model. Since the method uses random initialization, it never loses any information even when the selected features do not represent a motion model. However, this approach only works well when the independent moving object is big enough, such that it consists of enough features to properly estimate the object's motion. In addition, objects in the scene need to be in a certain size so that the background object features ratio is not too high in order for the object's features to be selected in the randomized features selection. Finally, its accuracy rate results can vary due to the random initialization.