Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Full Body Tracking from Multiple Views Using Stochastic Sampling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Real-Time 3D Articulated Pose Tracking using Particle Filters Interacting through Belief Propagation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Motion Tracking by Registering an Articulated Surface to 3D Points and Normals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multicamera tracking of articulated human motion using shape and motion cues
IEEE Transactions on Image Processing
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A 3D Time-of-flight (ToF) image is very useful to accurately track the human body motion due to its precision. However, the ToF image can not provide occluded 3D data because it also has a limitation of camera viewpoint. This paper proposes a self-occlusion handling scheme for human body motion tracking from 3D ToF image sequence. The proposed self-occlusion handling scheme consists of two steps: detect whether the body part is occluded or not and then estimate its motion from estimating the motion of non-occluded its adjacent body parts. Occlusion can be easily detected by using the eigenvalue analysis of 3D ToF data gathered from the joint point of each body part, and their motions can be estimated by calculating the rotation of the occluded body part. To apply it to the human body motion tracking, we use the Iterative closest point (ICP) algorithm and particle filter to track even the motion of fast moving body parts. Experimental results show that the human body motion tracking with the proposed self-occlusion handling scheme can correctly estimate even the motion of the self-occluded body part by comparing the estimated joint points with the manually marked joint points.