Compressive-sensing-based Human Action Recognition

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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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  -