tRANSAC: Dynamic feature accumulation across time for stable online RANSAC model estimation in automotive applications

Abstract

RANdom SAmple Consensus (RANSAC) is widely used in computer vision and automotive related applications. It is an iterative method to estimate parameters of mathematical model from a set of observed data that contains outliers. In computer vision, such observed data is usually a set of features (such as feature points, line segments) extracted from images. In automotive re-lated applications, RANSAC can be used to estimate lane vanishing point, camera view angles, ground plane etc. In such applications, changing content of road scene makes stable online model estimation difficult. In this paper, we propose a framework called tRANSAC to dynamically accumulate features across time so that online RANSAC model estimation can be stably performed. Feature accumulation across time is done in such a dynamic way that when RANSAC tends to perform robustly and stably, accumulated features are discarded fast so that fewer redundant features are used for RANSAC estimation; when RANSAC tends to perform poorly, accumulated features are discarded slowly so that more features can be used for better RANSAC estimation. Experimental results on road scene dataset for vanishing point and camera angle estimation show that the proposed tRANSAC method gives more stable and accurate estimates compared to baseline RANSAC method.