Proceedings of the 2016 International Conference on Engineering Science and Management

Compressive-sensing-based Human Action Recognition

Authors
Jun Jiang, Gao Li
Corresponding Author
Jun Jiang
Available Online August 2016.
DOI
10.2991/esm-16.2016.64How to use a DOI?
Keywords
Compressive sensing, hybrid fusion matrix, action recognition
Abstract

A new compressive sensing based dimensionality reduction method is proposed for human action recognition, in which a novel hybrid random matrix (HRM) is constructed and is proved to satisfy the restricted isometry property. It projects the high-dimensional features into a low-dimensional space via the HRM. Then the low-dimensional features are used for classification. Experimental results demonstrate that the proposed method is effective and efficient in human action recognition, and is on par with or better than the state-of-the-art methods.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Engineering Science and Management
Series
Advances in Engineering Research
Publication Date
August 2016
ISBN
978-94-6252-218-3
ISSN
2352-5401
DOI
10.2991/esm-16.2016.64How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jun Jiang
AU  - Gao Li
PY  - 2016/08
DA  - 2016/08
TI  - Compressive-sensing-based Human Action Recognition
BT  - Proceedings of the 2016 International Conference on Engineering Science and Management
PB  - Atlantis Press
SP  - 277
EP  - 279
SN  - 2352-5401
UR  - https://doi.org/10.2991/esm-16.2016.64
DO  - 10.2991/esm-16.2016.64
ID  - Jiang2016/08
ER  -