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Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation
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Luo, Jian and Tjahjadi, Tardi (2020) Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation. IEEE Access, 8 . pp. 32485-32501. doi:10.1109/ACCESS.2020.2973898 ISSN 2169-3536.
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WRAP-multi-set-canonical-correlation-analysis-3D-abnormal-gait-behaviour-recognition-based-virtual-sample-generation-Tjahjadi-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2067Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2020.2973898
Abstract
Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions.
Item Type: | Journal Article | |||||||||||||||||||||
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Subjects: | Q Science > QP Physiology T Technology > T Technology (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Three-dimensional modeling , Computer graphics , Gait in humans, Human-computer interaction, Human-computer interaction -- Simulation methods | |||||||||||||||||||||
Journal or Publication Title: | IEEE Access | |||||||||||||||||||||
Publisher: | IEEE | |||||||||||||||||||||
ISSN: | 2169-3536 | |||||||||||||||||||||
Official Date: | 13 February 2020 | |||||||||||||||||||||
Dates: |
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Volume: | 8 | |||||||||||||||||||||
Page Range: | pp. 32485-32501 | |||||||||||||||||||||
DOI: | 10.1109/ACCESS.2020.2973898 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
Date of first compliant deposit: | 4 March 2020 | |||||||||||||||||||||
Date of first compliant Open Access: | 6 March 2020 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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