Although, gaze-based interaction has been investigated since the 1980s and remains a promising concept to support universal interaction within distributed IoT environments, the main challenges, such as the Midas touch problem [Jac91] or calibration are still frequent topics of research. In this thesis, Natural Pursuit Calibration is presented, which is a comfortable, unobtrusive technique enabling ongoing attention detection and eye tracker calibration within an off-screen context. The user is able to perform calibration, without a digital user interface, artificial annotation of the environment and further assistance, by simply following any arbitrary moving target. Due to the characteristics of the calibration process, it can be executed simultaneously to any primary task, without active user participation. This then results in a frequently updated calibration model. A two-stage evaluation process is conducted to (i) optimize parameter settings in a first setup and (ii) compare the accuracy as well as the user acceptance of the proposed procedure to prevailing calibration techniques in an extensive user study.