한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제14권2호
  • /
  • Pages.182-193
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  • 2011
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  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
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Hand Tracking and Hand Gesture Recognition for Human Computer Interaction

  • Bai, Yu (Dept. of Information Communication Engineering, Tongmyong University) ;
  • Park, Sang-Yun (Dept. of Information Communication Engineering, Tongmyong University) ;
  • Kim, Yun-Sik (Dept. of Information Communication Engineering, Tongmyong University) ;
  • Jeong, In-Gab (Dept. of Car & Fire Station, Kyungpook Provincial College) ;
  • Ok, Soo-Yol (Dept. of Game Engineering, Tongmyong University) ;
  • Lee, Eung-Joo (Dept. of Information Communication Engineering, Tongmyong University)
  • 투고 : 2010.10.06
  • 심사 : 2010.12.06
  • 발행 : 2011.02.28
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초록

The aim of this paper is to present the methodology for hand tracking and hand gesture recognition. The detected hand and gesture can be used to implement the non-contact mouse. We had developed a MP3 player using this technology controlling the computer instead of mouse. In this algorithm, we first do a pre-processing to every frame which including lighting compensation and background filtration to reducing the adverse impact on correctness of hand tracking and hand gesture recognition. Secondly, YCbCr skin-color likelihood algorithm is used to detecting the hand area. Then, we used Continuously Adaptive Mean Shift (CAMSHIFT) algorithm to tracking hand. As the formula-based region of interest is square, the hand is closer to rectangular. We have improved the formula of the search window to get a much suitable search window for hand. And then, Support Vector Machines (SVM) algorithm is used for hand gesture recognition. For training the system, we collected 1500 hand gesture pictures of 5 hand gestures. Finally we have performed extensive experiment on a Windows XP system to evaluate the efficiency of the proposed scheme. The hand tracking correct rate is 96% and the hand gestures average correct rate is 95%.

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참고문헌

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  3. Face and Hand Activity Detection Based on Haar Wavelet and Background Updating Algorithm vol.14, pp.8, 2011, https://doi.org/10.9717/kmms.2011.14.8.992
  4. Face Tracking System Using Updated Skin Color vol.18, pp.5, 2015, https://doi.org/10.9717/kmms.2015.18.5.610
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