This paper deals with visual recognition and tracking of people and gestures from a camera mounted on a tour-guide robot in a human, cluttered, environment. The particle filtering framework enables the fusion of visual cues, both into an importance function from which the particles are sampled, and into a measurement model used for weights definition. The multi-cues associations prove to be more robust than any of the cues individually. For the purpose of gestures recognition, a tracker is proposed which handles multiple hand configurations templates. Finally, implementation and experiments on a tour-guide robot are presented in order to highlight the relevance and complementarity of the developed visual functions. Extensions are finally discussed.