Tracking Chessboard Corners Using Projective Transformation for Augmented Reality

Augmented reality has been a topic of intense research for several years for many applications. Itconsists of inserting a virtual object into a real scene. The virtual object must be accuratelypositioned in a desired place. Some measurements (calibration) are thus required and a set ofcorrespondences between points on the calibration target and the camera images must be found.In this paper, we present a tracking technique based on both detection of Chessboard cornersand a least squares method; the objective is to estimate the perspective transformation matrix forthe current view of the camera. This technique does not require any information or computation ofthe camera parameters; it can used in real time without any initialization and the user can changethe camera focal without any fear of losing alignment between real and virtual object.