Feature tracking is an important task in computer vision, especially for 3D reconstruction applications. Such procedures can be run in environments with a controlled sensor, e.g. a robot arm with camera. This yields the camera parameters as special knowledge that should be used during all steps of the application to improve the results. As a first step, KLT (Kanade-Lucas-Tomasi) tracking (and its variants) is an approach widely accepted and used to track image point features. So, it is straightforward to adapt KLT tracking in a way that camera parameters are used to improve the feature tracking results. The contribution of this work is an explicit formulation of the KLT tracking procedure incorporating known camera parameters. Since practical applications do not run without noise, the uncertainty of the camera parameters is regarded and modeled within the procedure of Guided KLT tracking (GKLT). Comparing practical experiments have been performed and the results are presented.