Thumbnail
Access Restriction
Open

Author Zhu, Youding ♦ Fujimura, Kikuo
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Natural sciences & mathematics ♦ Mathematics ♦ Life sciences; biology ♦ Physiology & related subjects ♦ Natural history of organisms ♦ Technology ♦ Medicine & health ♦ Human physiology ♦ Diseases ♦ Agriculture & related technologies ♦ Manufacture for specific uses ♦ Precision instruments & other devices
Subject Domain (in MeSH) Eukaryota ♦ Organisms ♦ Diagnosis ♦ Investigative Techniques ♦ Analytical, Diagnostic and Therapeutic Techniques and Equipment ♦ Physical Phenomena ♦ Musculoskeletal and Neural Physiological Phenomena ♦ Mathematical Concepts ♦ Biological Sciences ♦ Technology, Industry, and Agriculture ♦ Technology and Food and Beverages
Subject Keyword Discipline Biotechnology ♦ Imaging, Three-dimensional ♦ Methods ♦ Posture ♦ Algorithms ♦ Bayes Theorem ♦ Biomechanical Phenomena ♦ Humans ♦ Models, Anatomic ♦ Journal Article
Abstract 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 a depth image sequence is challenging due to the need to resolve the depth ambiguity caused by self-occlusions and the difficulty to recover from tracking failure. Human body poses could be estimated through model fitting using dense correspondences between depth data and an articulated human model (local optimization method). Although it usually achieves a high accuracy due to dense correspondences, it may fail to recover from tracking failure. Alternately, human pose may be reconstructed by detecting and tracking human body anatomical landmarks (key-points) based on low-level depth image analysis. While this method (key-point based method) is robust and recovers from tracking failure, its pose estimation accuracy depends solely on image-based localization accuracy of key-points. To address these limitations, we present a flexible Bayesian framework 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 approach.
Description Country affiliation: United States
Author Affiliation: Zhu Y ( Honda Research Institute USA, 800 California Street, Mountain View, CA 94041-2810, USA. zhu.81@buckeyemail.osu.edu)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-01-01
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 10
Issue Number 5


Source: WHO-Global Index Medicus