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Robust Hand Posture Recognition Integrating Multi-cue Hand Tracking

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Entertainment for Education. Digital Techniques and Systems (Edutainment 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6249))

Abstract

This paper proposes a robust real-time method for hand tracking and hand posture recognition. Dealing with complex background, scale-invariance and rotation-invariance are the difficulties for hand posture recognition. To solve these difficulties, we firstly detect the specific posture using the method based on Modified Census Transform, in order to trigger hand tracking and hand posture recognition. For the complex background particularly with large skin-color alike objects, a multi-cue method, based on velocity weighted features and color cue, is proposed to deal with the hand tracking. Then we segment the hand using both Bayesian skin-color model and the hand tracking result. Finally, we use a novel method based on density distribution feature to recognize hand posture. It largely enforces the robustness of hand posture recognition because of scale-invariance and rotation-invariance. Experiment results and applications demonstrate the effectiveness of our method.

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Author information

Authors and Affiliations

  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310027, China

    Chuanbo Weng, Yang Li, Mingmin Zhang, Kangde Guo & Zhigeng Pan

  2. Shanghai Research Center, Intel, Shanghai, China

    Xing Tang

Authors
  1. Chuanbo Weng
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  2. Yang Li
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  3. Mingmin Zhang
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  4. Kangde Guo
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  5. Xing Tang
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  6. Zhigeng Pan
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Editor information

Editors and Affiliations

  1. Institute of Automation, LIAMA, The Sino-French Lab in Computer Science, NLPR, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, 100080, Beijing, China

    Xiaopeng Zhang

  2. School of Software, Ideal Institute of Information and Technology, E-learning Laboratory of Jilin Province, Engineering & Research Center of E-learning, Northeast Normal University, 130117, ChangChun, China

    Shaochun Zhong

  3. State Key Lab of CAD&CG, DEARC – Digital Entertainment and Animation Research Center, Zhejiang University, 310027, Hangzhou, China

    Zhigeng Pan

  4. School of Information Technology, South St. Murdoch, Murdoch University, 6150, Perth, WA, Australia

    Kevin Wong

  5. School of Education and Science, Digital Entertainment Research Center, Nanjing Normal University, 310097, Nanjing, China

    Ruwei Yun

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© 2010 Springer-Verlag Berlin Heidelberg

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Weng, C., Li, Y., Zhang, M., Guo, K., Tang, X., Pan, Z. (2010). Robust Hand Posture Recognition Integrating Multi-cue Hand Tracking. In: Zhang, X., Zhong, S., Pan, Z., Wong, K., Yun, R. (eds) Entertainment for Education. Digital Techniques and Systems. Edutainment 2010. Lecture Notes in Computer Science, vol 6249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14533-9_51

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  • DOI: https://doi.org/10.1007/978-3-642-14533-9_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14532-2

  • Online ISBN: 978-3-642-14533-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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