Static hand gesture recognition with parallel CNNs for space human-robot interaction

Qing Gao; Jinguo Liu; Zhaojie Ju; Yangmin Li; Tian Zhang; Lu Zhang

doi:10.1007/978-3-319-65289-4_44

Static hand gesture recognition with parallel CNNs for space human-robot interaction

Qing Gao, Jinguo Liu, Zhaojie Ju, Yangmin Li, Tian Zhang, Lu Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

38 Citations (Scopus)

Abstract

As a new type of human-robot interaction (HRI), hand gesture has many advantages such as natural operation, rich expression and not subject to environ-mental constraints. So it is very suitable for space human-robot interaction tasks in special and harsh environment. Considering that static hand gesture is one of the main gesture expressions in human-computer interaction, so a parallel convolution neural networks (CNNs) is designed to improve the accuracy of static hand gesture recognition in the conditions of complex background and changing illumination. In addition, the method is applied to the operation of space human-robot system with hand gesture control. Various space HRI hand gestures from different subjects are evaluated and tested, and experimental results demonstrate that the proposed method outperforms the single-channel CNN methods and other popular methods with a higher accuracy.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings
Publisher	Springer Verlag
Pages	462-473
Number of pages	12
ISBN (Print)	9783319652887
DOIs	https://doi.org/10.1007/978-3-319-65289-4_44
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	10th International Conference on Intelligent Robotics and Applications, ICIRA 2017 - Wuhan, China Duration: 16 Aug 2017 → 18 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10462 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Intelligent Robotics and Applications, ICIRA 2017
Country/Territory	China
City	Wuhan
Period	16/08/17 → 18/08/17

Keywords

CNN
Hand gesture recognition
HRI
Space robot

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-319-65289-4_44

Cite this

Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., & Zhang, L. (2017). Static hand gesture recognition with parallel CNNs for space human-robot interaction. In Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings (pp. 462-473). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10462 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-65289-4_44

Gao, Qing ; Liu, Jinguo ; Ju, Zhaojie et al. / Static hand gesture recognition with parallel CNNs for space human-robot interaction. Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings. Springer Verlag, 2017. pp. 462-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Gao, Q, Liu, J, Ju, Z, Li, Y, Zhang, T & Zhang, L 2017, Static hand gesture recognition with parallel CNNs for space human-robot interaction. in Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10462 LNAI, Springer Verlag, pp. 462-473, 10th International Conference on Intelligent Robotics and Applications, ICIRA 2017, Wuhan, China, 16/08/17. https://doi.org/10.1007/978-3-319-65289-4_44

Static hand gesture recognition with parallel CNNs for space human-robot interaction. / Gao, Qing; Liu, Jinguo; Ju, Zhaojie et al.
Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings. Springer Verlag, 2017. p. 462-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10462 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Liu, Jinguo

AU - Ju, Zhaojie

AU - Li, Yangmin

AU - Zhang, Tian

AU - Zhang, Lu

PY - 2017/1/1

Y1 - 2017/1/1

N2 - As a new type of human-robot interaction (HRI), hand gesture has many advantages such as natural operation, rich expression and not subject to environ-mental constraints. So it is very suitable for space human-robot interaction tasks in special and harsh environment. Considering that static hand gesture is one of the main gesture expressions in human-computer interaction, so a parallel convolution neural networks (CNNs) is designed to improve the accuracy of static hand gesture recognition in the conditions of complex background and changing illumination. In addition, the method is applied to the operation of space human-robot system with hand gesture control. Various space HRI hand gestures from different subjects are evaluated and tested, and experimental results demonstrate that the proposed method outperforms the single-channel CNN methods and other popular methods with a higher accuracy.

AB - As a new type of human-robot interaction (HRI), hand gesture has many advantages such as natural operation, rich expression and not subject to environ-mental constraints. So it is very suitable for space human-robot interaction tasks in special and harsh environment. Considering that static hand gesture is one of the main gesture expressions in human-computer interaction, so a parallel convolution neural networks (CNNs) is designed to improve the accuracy of static hand gesture recognition in the conditions of complex background and changing illumination. In addition, the method is applied to the operation of space human-robot system with hand gesture control. Various space HRI hand gestures from different subjects are evaluated and tested, and experimental results demonstrate that the proposed method outperforms the single-channel CNN methods and other popular methods with a higher accuracy.

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Gao Q, Liu J, Ju Z, Li Y, Zhang T, Zhang L. Static hand gesture recognition with parallel CNNs for space human-robot interaction. In Intelligent Robotics and Applications - 10th International Conference, ICIRA 2017, Proceedings. Springer Verlag. 2017. p. 462-473. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-65289-4_44

Static hand gesture recognition with parallel CNNs for space human-robot interaction

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Keywords

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