Generalized preemptive RANSAC making preemptive RANSAC feasible even in low resources devices

This paper examines a generalized version of Preemptive RANSAC for visual motion estimation. The approach described employs the BRUMA function for dealing with varying block sizes and the percentages of hypotheses to be removed during the hypotheses rejection phase. The generation of a flexible number of hypotheses is also performed in order to balance the preemption scheme. Experiments were performed for both forward and side-wise motions in synthetic environment by using simulation and the ground-truth used to compare the Standard Preemptive RANSAC and its generalized version. Simulations confirmed that the quality of the results produced by the Standard Preemptive RANSAC degrade as the hardware resources used are decreased, as opposed to the results produced by the Generalized Preemptive RANSAC, with the results of the Standard Preemptive RANSAC having errors up to eleven times larger than the Generalized Preemptive RANSAC.