The movement of whiskers in head-fixed mice is of high interest for neurological research, as it allows scientists to learn more about learning processes during active touch. However, manual tracking of whiskers in thousands of frames is not feasible, and reliable tracking of individual whiskers is not possible with the best current software available. In this work, a new system for the automatic tracking of whiskers in top-view videos of untrimmed, head-fixed mice is presented. The design of this system is based on a variety of image processing and computer vision algorithms. For each step, several alternatives have been considered and assessed. The best options have been combined and implemented in MATLAB. This new system, the Full-Whisker Tracking System (FWTS), detects the centerline of whiskers on each frame along their whole length with sub-pixel accuracy. The whiskers are defined by their angle, position, shape and length. FWTS uses these features to track and recognize whiskers over multiple frames in video segments with a frame rate of 1000 Hz. The system was tested on representative, challenging video segments from two experiments using different mice, and showed itself superior to the pre-existing system BWTT, by detecting on average 21.2% more whiskers, and with twice as much precision. In contrast to earlier systems, FWTS has been demonstrated reliably track individual whiskers in untrimmed mice. Furthermore, FWTS's processing time is on average 41.7% lower than that of BWTT. This thesis work shows that it is possible to track whiskers in untrimmed mice, even in the face of partial occlusions, and makes it possible to study the functioning of the intact whisker system of mice in unprecedented detail.