Constrained Optimization for Human Pose Estimation from Depth Sequences

A new 2-step method is presented for human upper-body pose estimation from depth sequences, in which coarse human part labeling takes place first, followed by more precise joint position estimation as the second phase. In the first step, a number of constraints are extracted from notable image features such as the head and torso. The problem of pose estimation is cast as that of label assignment with these constraints. Major parts of the human upper body are labeled by this process. The second step estimates joint positions optimally based on kinematic constraints using dense correspondences between depth profile and human model parts. The proposed framework is shown to overcome some issues of existing approaches for human pose tracking using similar types of data streams. Performance comparison with motion capture data is presented to demonstrate the accuracy of our approach.

[1]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  John P. Lewis,et al.  Pose Space Deformation: A Unified Approach to Shape Interpolation and Skeleton-Driven Deformation , 2000, SIGGRAPH.

[3]  Éva Tardos,et al.  Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields , 2002, JACM.

[4]  Takeo Kanade,et al.  Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  Cristian Sminchisescu,et al.  Kinematic jump processes for monocular 3D human tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Sebastian Thrun,et al.  Recovering Articulated Object Models from 3D Range Data , 2004, UAI.

[7]  Samuel R. Buss,et al.  Selectively Damped Least Squares for Inverse Kinematics , 2005, J. Graph. Tools.

[8]  Trevor Darrell,et al.  Avoiding the "streetlight effect": tracking by exploring likelihood modes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[9]  Reinhard Koch,et al.  Nonlinear Body Pose Estimation from Depth Images , 2005, DAGM-Symposium.

[10]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[11]  Rüdiger Dillmann,et al.  Sensor fusion for 3D human body tracking with an articulated 3D body model , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[12]  Julius Ziegler,et al.  Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).