A robust gesture recognition based on depth data

Lee Jaemin; Hironori Takimoto; Hitoshi Yamauchi; Akihiro Kanazawa; Yasue Mitsukura

doi:10.1109/FCV.2013.6485474

A robust gesture recognition based on depth data

Lee Jaemin, Hironori Takimoto, Hitoshi Yamauchi, Akihiro Kanazawa, Yasue Mitsukura

Department of System Design Engineering

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

18 Citations (Scopus)

Abstract

In this paper, we propose a novel method for gesture recognition using depth data captured by Microsoft Kinect sensor. Conventionally, the features which have been used for gesture recognition are divided into two parts, hand shape and arm movement. In conventional methods, only two-dimensional hand features are used because human's hand consists of the multiple joint structure. Furthermore, conventional arm movement feature are influenced by environmental changing, such as individual differences in body size, camera position and so on. Therefore, to assist in the recognition, a method of feature extraction is proposed, which involves the hand shape with 3D feature and the arm movement with angle between joints of body. In order to show effectiveness of the proposed method, performance for gesture recognition is compared with conventional methods using Japanese language.

Original language	English
Title of host publication	FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision
Pages	127-131
Number of pages	5
DOIs	https://doi.org/10.1109/FCV.2013.6485474
Publication status	Published - 2013 Apr 15
Event	19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013 - Incheon, Korea, Republic of Duration: 2013 Jan 30 → 2013 Feb 1

Publication series

Name	FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision

Other

Other	19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013
Country/Territory	Korea, Republic of
City	Incheon
Period	13/1/30 → 13/2/1

Keywords

HMM
Image processing
depth sensor
hand geesture recognition

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/FCV.2013.6485474

Cite this

Jaemin, L., Takimoto, H., Yamauchi, H., Kanazawa, A., & Mitsukura, Y. (2013). A robust gesture recognition based on depth data. In FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision (pp. 127-131). [6485474] (FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision). https://doi.org/10.1109/FCV.2013.6485474

A robust gesture recognition based on depth data. / Jaemin, Lee; Takimoto, Hironori; Yamauchi, Hitoshi et al.

FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision. 2013. p. 127-131 6485474 (FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision).

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

Jaemin, L, Takimoto, H, Yamauchi, H, Kanazawa, A & Mitsukura, Y 2013, A robust gesture recognition based on depth data. in FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision., 6485474, FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 127-131, 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, FCV 2013, Incheon, Korea, Republic of, 13/1/30. https://doi.org/10.1109/FCV.2013.6485474

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title = "A robust gesture recognition based on depth data",

abstract = "In this paper, we propose a novel method for gesture recognition using depth data captured by Microsoft Kinect sensor. Conventionally, the features which have been used for gesture recognition are divided into two parts, hand shape and arm movement. In conventional methods, only two-dimensional hand features are used because human's hand consists of the multiple joint structure. Furthermore, conventional arm movement feature are influenced by environmental changing, such as individual differences in body size, camera position and so on. Therefore, to assist in the recognition, a method of feature extraction is proposed, which involves the hand shape with 3D feature and the arm movement with angle between joints of body. In order to show effectiveness of the proposed method, performance for gesture recognition is compared with conventional methods using Japanese language.",

keywords = "HMM, Image processing, depth sensor, hand geesture recognition",

author = "Lee Jaemin and Hironori Takimoto and Hitoshi Yamauchi and Akihiro Kanazawa and Yasue Mitsukura",

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N2 - In this paper, we propose a novel method for gesture recognition using depth data captured by Microsoft Kinect sensor. Conventionally, the features which have been used for gesture recognition are divided into two parts, hand shape and arm movement. In conventional methods, only two-dimensional hand features are used because human's hand consists of the multiple joint structure. Furthermore, conventional arm movement feature are influenced by environmental changing, such as individual differences in body size, camera position and so on. Therefore, to assist in the recognition, a method of feature extraction is proposed, which involves the hand shape with 3D feature and the arm movement with angle between joints of body. In order to show effectiveness of the proposed method, performance for gesture recognition is compared with conventional methods using Japanese language.

AB - In this paper, we propose a novel method for gesture recognition using depth data captured by Microsoft Kinect sensor. Conventionally, the features which have been used for gesture recognition are divided into two parts, hand shape and arm movement. In conventional methods, only two-dimensional hand features are used because human's hand consists of the multiple joint structure. Furthermore, conventional arm movement feature are influenced by environmental changing, such as individual differences in body size, camera position and so on. Therefore, to assist in the recognition, a method of feature extraction is proposed, which involves the hand shape with 3D feature and the arm movement with angle between joints of body. In order to show effectiveness of the proposed method, performance for gesture recognition is compared with conventional methods using Japanese language.

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A robust gesture recognition based on depth data

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Keywords

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