This paper introduces a new object tracking method which combines two algorithms working in parallel, and based on low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and the Particle Filter, using a global description. The object model is updated by combining information from a back-projection map computed from the Generalised Hough Transform, providing for every pixel the degree to which it may belong to the object, and from the Particle Filter, providing a probability density on the global object position. The proposed tracker makes the most of the two algorithms, in terms of robustness to appearance variation like scaling, rotation, non-rigid deformation or illumination changes.