A fast and robust homography scheme for real-time planar target detection

Abstract

The present paper is concerned with the problem of robust pose estimation for planar targets in the context of real-time mobile vision. For robust recognition of targets at very low computational costs, we employ feature-based methods which are based on local binary descriptors allowing fast feature matching at run time. The matching set is then fed to a robust parameter estimation algorithm to obtain a reliable estimate of homography. The robust estimation of model parameters, which in our case is a 2D homographic transformation, constitutes an essential part of the whole recognition process. We present a highly optimized and device-friendly implementation of homography estimation through a unified hypothesize-and-verify framework. This framework is specifically designed to meet the growing demand for fast and robust estimation on power-constrained platforms. The focus of the approach described in this paper is not only on developing fast algorithms for the recognition framework, but also on the optimized implementation of such algorithms by accounting for the computing capacity of modern CPUs. The experimentations show that the resulting homography estimation implementation proposed in this paper brings a speedup of 25 $$\times$$ over the regular OpenCV RANSAC homography estimation function.