This paper addresses the problem of 3D tracking of human gesture for buying behavior estimation. The top view of the customers, which has been little treated for human tracking, is exploited in this particular context. This point of view avoids occlusion except for those of the arms. We propose an hybrid 3D-2D tracking method based on the particle filtering framework, which uses the exclusion principle to separate the observation related to each customer and deals with multi-person tracking. The head and shoulders are tracked in the 2D space, while the arms are tracked in the 3D space: these are the spaces where they are the most descriptive. We validate our method both experimentally, so as to obtain qualitative results, and on-site. We demonstrated that it makes a good estimation for various cases and situations in real-time ((Formula presented.)40 fps).