Markerless human tracking addresses the problem of estimating human body motion in non-cooperative environments. Computer Vision techniques combined with Pattern Recognition theory serve the purpose of extracting information on human body postures from video-sequences, without the need of wearable markers. Multi-camera systems further enhance this kind of application providing frames from multiple viewpoints. This work tackles the application of multi-camera posture estimation through the use of a multi-camera environment, also known as "smart space". A 3D skeleton structure and geometrical descriptors of human muscles are fitted to the volumetric data to directly recover 3D information. 3D skeleton deformations and bio-mechanical constraints on joint models are used to provide posture information at each frame. The proposed system does not require any pre-initialization phase and automatically adapt the skeleton and the volumetric occupation of each limb to the actor physiognomy independently from the pose. Exhaustive tests were performed to validate our approach.