Vehicle-type motion estimation by the fusion of image point and line features

Abstract

Three-dimensional (3D) motion estimation is a very important topic in machine vision. However, reliability of the estimated 3D motion seems to be the most challenging problem, especially to the linear algorithms developed for solving a general 3D motion problem (six degrees of freedom). In real applications such as the traffic surveillance and auto-vehicle systems, the observed 3D motion has only three degrees of freedom because of the ground plane constraint (GPC). In this paper, a new iterative method is proposed for solving the above problem. Our method has several advantages: (1) It can handle both the point and line features as its input image data. (2) It is very suitable for parallel processing. (3) Its cost function is so well-conditioned that the final 3D motion estimation is robust and insensitive to noise, which is proved by experiments. (4) It can handle the case of missing data to a certain degree. The above benefits make our method suitable for a real application. Experiments including simulated and real-world images show satisfactory results.