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Vision-Based Human Gesture Recognition Using Kinect Sensor

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The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 291))

Abstract

Gestures are indeed important in our daily life as they serve as one of the communication platform by using body motions in order to deliver information or effectively interact. This paper proposes to leverage the Kinect sensor for close-range human gesture recognition. The orientation details of human arms are extracted from the skeleton map sequences in order to form a bag of quaternions feature vectors. After the conversion to log-covariance matrix, the system is trained and the gestures are classified by multi-class SVM classifier. An experimental dataset of skeleton map sequences for 5 subjects with 6 gestures was collected and tested. The proposed system obtained remarkably accurate result with nearly 99 % of average correct classification rate (ACCR) compared to state of the art method with ACCR of 95 %.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Bukit Beruang, Melaka, Malaysia

    Huong Yong Ting, Kok Swee Sim, Fazly Salleh Abas & Rosli Besar

Authors
  1. Huong Yong Ting
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  2. Kok Swee Sim
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  3. Fazly Salleh Abas
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  4. Rosli Besar
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Corresponding author

Correspondence to Huong Yong Ting .

Editor information

Editors and Affiliations

  1. School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia

    Harsa Amylia Mat Sakim

  2. School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia

    Mohd Tafir Mustaffa

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© 2014 Springer Science+Business Media Singapore

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Ting, H.Y., Sim, K.S., Abas, F.S., Besar, R. (2014). Vision-Based Human Gesture Recognition Using Kinect Sensor. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_28

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  • DOI: https://doi.org/10.1007/978-981-4585-42-2_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4585-41-5

  • Online ISBN: 978-981-4585-42-2

  • eBook Packages: EngineeringEngineering (R0)

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