Articulated Soft Objects for Multiview Shape and Motion Capture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraining Human Body Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-Time Body Pose Recognition Using 2D or 3D Haarlets
International Journal of Computer Vision
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Nonlinear body pose estimation from depth images
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Bayesian 3d human body pose tracking from depth image sequences
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Fast articulated motion tracking using a sums of Gaussians body model
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A data-driven approach for real-time full body pose reconstruction from a depth camera
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Efficient regression of general-activity human poses from depth images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Eyes-free yoga: an exergame using depth cameras for blind & low vision exercise
Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility
Dynamics based 3D skeletal hand tracking
Proceedings of Graphics Interface 2013
Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image
Proceedings of the Fourth Symposium on Information and Communication Technology
Special Section on CAD/Graphics 2013: SCAPE-based human performance reconstruction
Computers and Graphics
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Tracking human pose in real-time is a difficult problem with many interesting applications. Existing solutions suffer from a variety of problems, especially when confronted with unusual human poses. In this paper, we derive an algorithm for tracking human pose in real-time from depth sequences based on MAP inference in a probabilistic temporal model. The key idea is to extend the iterative closest points (ICP) objective by modeling the constraint that the observed subject cannot enter free space, the area of space in front of the true range measurements. Our primary contribution is an extension to the articulated ICP algorithm that can efficiently enforce this constraint. The resulting filter runs at 125 frames per second using a single desktop CPU core. We provide extensive experimental results on challenging real-world data, which show that the algorithm outperforms the previous state-of-the-art trackers both in computational efficiency and accuracy.