This paper deals with the problem of 3D human tracking in catadioptric images using particle-filtering framework. While traditional perspective images are well exploited, only a few methods have been developed for catadioptric vision, for the human detection or tracking problems. We propose to extend the 3D pose estimation in the case of perspective cameras to catadioptric sensors. In this paper, we develop an original likelihood functions based, on the one hand, on the geodetic distance in the spherical space SO3 and, on the other hand, on the mapping between the human silhouette in the images and the projected 3D model. These likelihood functions combined with a particle filter, whose propagation model is adapted to the spherical space, allow accurate 3D human tracking in omnidirectional images. Both visual and quantitative analysis of the experimental results demonstrate the effectiveness of our approach.