While most vision systems for tracking people on mobile robots use skin color information, we present an approach using thermal images and two different measurement models together with a Particle Filter. With this method a person can be detected independently from current light conditions and in situations were no skin color is visible (the person is not close or does not face the robot). The results show that a measurement model that was learned from local greyscale features improved on the performance of an elliptic contour model, and that both models could be used in combination to further improve performance with minimal extra computational cost