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Model-Based Feature Extraction for Gait Analysis and Recognition

Model-Based Feature Extraction for Gait Analysis and Recognition
Model-Based Feature Extraction for Gait Analysis and Recognition
Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications. We propose a new approach to extract human joints (vertex positions) using a model-based method. Motion templates describing the motion of the joints as derived by gait analysis, are parametrized using the elliptic Fourier descriptors. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the proposed method to recognize walking subjects with a correct classification rate of %92.
Model-based feature extraction, motion analysis, gait, gait analysis
150-160
BOUCHRIKA, I
b294dfd3-6686-49b1-af2f-0ed0ebc3c5f5
NIXON, M S
2b5b9804-5a81-462a-82e6-92ee5fa74e12
BOUCHRIKA, I
b294dfd3-6686-49b1-af2f-0ed0ebc3c5f5
NIXON, M S
2b5b9804-5a81-462a-82e6-92ee5fa74e12

BOUCHRIKA, I and NIXON, M S (2007) Model-Based Feature Extraction for Gait Analysis and Recognition. Mirage: Computer Vision / Computer Graphics Collaboration Techniques and Applications, France. pp. 150-160 .

Record type: Conference or Workshop Item (Paper)

Abstract

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications. We propose a new approach to extract human joints (vertex positions) using a model-based method. Motion templates describing the motion of the joints as derived by gait analysis, are parametrized using the elliptic Fourier descriptors. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the proposed method to recognize walking subjects with a correct classification rate of %92.

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More information

Published date: March 2007
Additional Information: Event Dates: March, 2007
Venue - Dates: Mirage: Computer Vision / Computer Graphics Collaboration Techniques and Applications, France, 2007-03-01
Keywords: Model-based feature extraction, motion analysis, gait, gait analysis
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263953
URI: https://eprints.soton.ac.uk/id/eprint/263953
PURE UUID: 16f1d916-7725-4f86-9b90-40c812ddae3e
ORCID for M S NIXON: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 01 May 2007
Last modified: 07 Aug 2019 00:54

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