Multiview 3D Human Motion Tracking with Soft-Joint Constrained ICP
Date Issued
2008
Date
2008
Author(s)
Chu, Lu-Jong
Abstract
In this thesis, we aim to track 3D human motions in image sequences captured from multiple cameras. The target motion is not limited to specific kinds of human motions, such as walking or jogging, that is, there is no restrictions imposed on possible human motions. Because self-occlusion and depth ambiguity occur easily when using only one single camera, we obtain multiple videos captured with multiple cameras from different viewpoints to reconstruct 3D shape volume of the target subject, which is an effective way to integrate information from multiple views.e propose a hierarchical human motion tracking method that can effectively capture human articulated motions with high degrees of freedom (DOFs). At each time step, the torso motion is estimated first and then the estimation of the limbs motions is carried out individually. The particle filtering, which is a popular method for high dimensional tracking, is adopted to track the torso motion because it can deal with the nonlinear and multimodal posterior probability distributions.ne disadvantage of hierarchical human motion tracking is that torso tracking errors may deteriorate limbs motion estimation. To reduce the interference from inaccurate torso motions, we propose a soft-joint constrained ICP (Iterative Closest Point) method to estimate limb motions. In contrast to hard joints, limbs with soft joints are allowed to move freely in a small range of area, so it is still possible to track limb motions even with inaccurate torso motions. However, the DOFs of each limb increase from 4 to 7 when the soft-joint constraint is used. The proposed soft-joint constrained ICP can efficiently determines 6 DOFs such that only 1 DOF (elbow/knee) is left for the particle filtering. Integrating the advantages of particle filtering and soft-joint constrained ICP at the same time, our method can effectively track limb motions even when there is large motion in a short period of time.oreover, we find that the torso motion is strongly related to the limbs motions. If the states of the four limbs are known, it is usually possible to predict the torso state without other information, especially when the limbs states are reliable. In order to improve torso motion tracking, the limbs motions estimated at the previous time step can provide reliable hypotheses of current torso state which is implemented as sampling particles from limbs states for torso tracking. We have conducted experiments with multiple video sequences of different motions, and the results show that our method is effective and reliable for 3D human motion tracking.
Subjects
human motion tracking
particle filtering
pose estimation
ICP
multiview
3D human model
volume reconstruction
Type
thesis
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