This thesis presents a method for performing tracking and estimation of head position and orientation by means of template based particle filtering. The implementation is designed to withstand high levels of occlusion and noise, and allow for system dynamics to be accounted for. To accelerate the computation, GPGPU techniques are used to enable the GPU to function a co-processor, resulting in real-time performance. A method is devised for dynamic creation of feature points used in the particle filter. Furthermore, the graphics pipeline is used to overlay and visualize the tracking, as well as play a key role in the dynamic template functionality. Finally, a benchmarking system is suggested and developed for carrying out controlled evaluation of tracking methods in general.