3D gait estimation from monoscopic video

This paper presents a new approach for 3D gait estimation from monocular image sequences, using both a kinematics and a walking motion models as sources of prior knowledge. The proposed technique consists of two major stages. Firstly, the motion trajectory and the pedestrian's footprints are detected throughout the segmented video sequence. Secondly, as the 3D human model, driven by the prior motion model, walks over this trajectory, the joints' angles are locally adjusted to the pedestrian's walking style. This tuning process is performed once per walking cycle and not per frame, saving considerable CPU time. In addition, local tuning allows handling displacements at different speeds or directions. The target application is the augmentation of 2D television sequences with depth information that may be used in future 3D-TV systems.