This paper addresses the problem of the tracking of 3D human body pose from depth image sequences given by a Xtion Pro-Live camera. Human body poses could be estimated through model tting using dense correspondences between depth data and an articulated human model. Although, most of the time for the video surveillance, the camera is placed above the persons, all the tracking methods use the front view. Indeed the human shape is more discriminative in this view. We propose a new model to be tted to the top view in a particle lter framework for a real-time markerless tracking. The model is composed of two parts: a 2D model providing the human localization and a 3D model providing its pose. There are few wrong estimations and they are e ciently detected by a con dence measure.