Calibration Between Eye Tracker and Stereoscopic Vision System Employing a Linear Closed-Form Perspective-n-Point (PNP) Algorithm
Abstract
In many advanced driver assistance systems (ADAS) applications, it is essential to figure out where gaze of driver locates in image area of stereoscopic vision system. This problem, which involves a cross calibration between the stereo vision system and eye tracker, is a challenging task since the two systems are not consistent in modality and do not share a common image area. The eye tracker system provides a 3D gaze vector which describes the direction of driver’s 3D line of gaze, while the stereoscopic vision system provides a depth image frame. In this thesis, this crosscalibration was performed with a closed-form solution that employs an efficient, linear time Perspective-n-Point algorithm. The main contribution of the present thesis is reformulation of this cross-calibration problem in a way that we would be able to employ PnP algorithm for providing a closed-form solution. The calibration process maps the 3D driver gaze vector into the surrounding outdoor environment. Moreover, the robustness of the algorithm with respect to noise is investigated on a set of synthetic data as well as in a lab-environment place. Keywords: Cross-calibration, perspective-n-point, driver