A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Singularity Analysis for Articulated Object Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Avoiding the "Streetlight Effect": Tracking by Exploring Likelihood Modes
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Constrained optimization for human pose estimation from depth sequences
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Proposal maps driven MCMC for estimating human body pose in static images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 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
Real-time human pose tracking from range data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Using Segmented 3D Point Clouds for Accurate Likelihood Approximation in Human Pose Tracking
International Journal of Computer Vision
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This paper addresses the problem of accurate and robust tracking of 3D human body pose from depth image sequences. Recovering the large number of degrees of freedom in human body movements from depth image sequence is challenging due to the need to resolve depth ambiguity caused by self-occlusions and difficulty to recover from tracking failure. Human body poses could be estimated with a high accuracy based on local optimization using dense correspondences between 3D depth data and the vertices in an articulated human model. However, it cannot recover from tracking failure. This paper presents a method to reconstruct human pose by detecting and tracking human body anatomical landmarks (key-points) from depth images. The proposed method is robust and recovers from tracking failure when a body part is re-detected. However, its pose estimation accuracy depends solely on image-based localization accuracy of key-points. To address these limitations, we present a flexible Bayesian method for integrating pose estimation results obtained by methods based on key-points and local optimization. Experimental results are shown and performance comparison is presented to demonstrate the effectiveness of the proposed method.