Self-occlusion handling for human body motion tracking from 3D ToF image sequence
Proceedings of the 1st international workshop on 3D video processing
Probabilistic deformable surface tracking from multiple videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Predicting Articulated Human Motion from Spatial Processes
International Journal of Computer Vision
Skin colour segmentation based 2D and 3D human pose modelling using Discrete Wavelet Transform
Pattern Recognition and Image Analysis
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns
International Journal of Computer Vision
The role of physical controllers in motion video gaming
Proceedings of the Designing Interactive Systems Conference
Performance capture of interacting characters with handheld kinects
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
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We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of an articulated object, as well as probabilities that the data are assigned either to an object part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes.