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Non-linear Statistical Models for the 3D Reconstruction of Human Pose and Motion from Monocular Image Sequences

Bowden, R, Mitchell, TA and Sarhadi, M (2000) Non-linear Statistical Models for the 3D Reconstruction of Human Pose and Motion from Monocular Image Sequences Image and Vision Computing, 18 (9). pp. 729-737.

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Abstract

This paper presents a model based approach to human body tracking in which the 2D silhouette of a moving human and the corresponding 3D skeletal structure are encapsulated within a non-linear point distribution model. This statistical model allows a direct mapping to be achieved between the external boundary of a human and the anatomical position. It is shown how this information, along with the position of landmark features such as the hands and head can be used to reconstruct information about the pose and structure of the human body from a monocular view of a scene.

Item Type: Article
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Bowden, RUNSPECIFIEDUNSPECIFIED
Mitchell, TAUNSPECIFIEDUNSPECIFIED
Sarhadi, MUNSPECIFIEDUNSPECIFIED
Date : June 2000
Identification Number : https://doi.org/10.1016/S0262-8856(99)00076-1
Copyright Disclaimer : © 2000 Elsevier Science B.V. All rights reserved.
Uncontrolled Keywords : Human body tracking, Non-linear point distribution model, Statistical model, Pose reconstruction
Depositing User : Symplectic Elements
Date Deposited : 27 Oct 2016 12:11
Last Modified : 27 Oct 2016 12:11
URI: http://epubs.surrey.ac.uk/id/eprint/812637

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