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Tracking of Human Body Parts using the Multiocular Contracting Curve Density Algorithm

Markus Hahn, Lars Kruger, Christian Wohler, Horst-Michael Gross
2007 3-D Digital Imaging and Modeling  
In this contribution we introduce the Multiocular Contracting Curve Density algorithm (MOCCD), a novel method for fitting a 3D parametric curve.  ...  The MOCCD is integrated into a tracking system and its suitability for tracking human body parts in 3D in front of cluttered background is examined.  ...  A new 3D pose estimation method, the Multiocular Contracting Curve Density algorithm (MOCCD), is introduced and applied to track the 3D pose of the hand-forearm limb with a traditional Kalman Filter framework  ... 
doi:10.1109/3dim.2007.59 dblp:conf/3dim/HahnKWG07 fatcat:7uhor4etqfc5dc5p6boeazulx4

Quasi-Monte and Data-Driven Monte Carlo Methods for Efficient Human Joint Model Fitting

Sara Greenberg, John McPhee, Alexander Wong
1969 Journal of Computational Vision and Imaging Systems  
Fitting a kinematic model of the human body to an image withoutthe use of markers is a method of pose estimation that is usefulfor tracking and posture evaluation.  ...  This model-fitting is challengingdue to the variation in human physique and the large numberof possible poses. One type of modeling is to represent the humanbody as a set of rigid body volumes.  ...  Hahn et. al [5] propose a 3D model-fitting approach for a robot or a human arm using ICP and the Multiocular Contracting Curve Density (MOCCD) in a multi-camera environment and achieved results averaging  ... 
doi:10.15353/vsnl.v2i1.124 fatcat:oxwu4cdxnzdx3ojuuvcsuyyefm