Heteroscedastic Hough Transform (HtHT): An Efficient Method for Robust Line Fitting in the Errors in the Variables Problem

Given a sequence of image pairs we describe a method that segments the observed scene into static and moving objects while it rejects badly matched points. We show that, using a moving stereo rig, the detection of motion can be solved in a projective framework and therefore requires no camera calibration. Moreover the method allows for articulated objects. First we establish the projective framework enabling us to characterize rigid motion in projective space. This characterization is used in conjunction with a robust estimation technique to determine egomotion. Second we describe a method based on data classification which further considers the non-static scene points and groups them into several moving objects. Third we introduce a stereo-tracking algorithm that provides the point-to-point correspondences needed by the algorithms. Finally we show some experiments involving a moving stereo head observing both static and moving objects.