An enhanced multi-view human action recognition system for virtual training simulator

Beom Kwon; Junghwan Kim; Sanghoon Lee

doi:10.1109/APSIPA.2016.7820895

An enhanced multi-view human action recognition system for virtual training simulator

Beom Kwon, Junghwan Kim, Sanghoon Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.

Original language	English
Title of host publication	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9789881476821
DOIs	https://doi.org/10.1109/APSIPA.2016.7820895
Publication status	Published - 2017 Jan 17
Event	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of Duration: 2016 Dec 13 → 2016 Dec 16

Publication series

Name	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country/Territory	Korea, Republic of
City	Jeju
Period	16/12/13 → 16/12/16

Bibliographical note

Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Information Systems
Signal Processing

Access to Document

10.1109/APSIPA.2016.7820895

Cite this

Kwon, B., Kim, J., & Lee, S. (2017). An enhanced multi-view human action recognition system for virtual training simulator. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 Article 7820895 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820895

Kwon, Beom ; Kim, Junghwan ; Lee, Sanghoon. / An enhanced multi-view human action recognition system for virtual training simulator. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

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abstract = "Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.",

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Kwon, B, Kim, J & Lee, S 2017, An enhanced multi-view human action recognition system for virtual training simulator. in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016., 7820895, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Jeju, Korea, Republic of, 16/12/13. https://doi.org/10.1109/APSIPA.2016.7820895

An enhanced multi-view human action recognition system for virtual training simulator. / Kwon, Beom; Kim, Junghwan; Lee, Sanghoon.
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820895 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Kwon B, Kim J, Lee S. An enhanced multi-view human action recognition system for virtual training simulator. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820895. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). doi: 10.1109/APSIPA.2016.7820895

An enhanced multi-view human action recognition system for virtual training simulator

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