Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Part-based Hand Detection Using HOG

HOG를 이용한 파트 기반 손 검출 알고리즘

  • Baek, Jeonghyun (School of Electrical and Electronics Engineering, Yonsei University) ;
  • Kim, Jisu (School of Electrical and Electronics Engineering, Yonsei University) ;
  • Yoon, Changyong (Department of Electrical Engineering, Suwon Science College) ;
  • Kim, Dong-Yeon (Electrical, Electronic and Control Engineering, Hankyong National University) ;
  • Kim, Euntai (School of Electrical and Electronics Engineering, Yonsei University)
  • 백정현 (연세대학교 전기전자공학부) ;
  • 김지수 (연세대학교 전기전자공학부) ;
  • 윤창용 (수원과학대학교 전기공학과) ;
  • 김동연 (한경대학교 전기전자제어공학과) ;
  • 김은태 (연세대학교 전기전자공학부)
  • Received : 2013.09.01
  • Accepted : 2013.11.25
  • Published : 2013.12.25
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Abstract

In intelligent robot research, hand gesture recognition has been an important issue. And techniques that recognize simple gestures are commercialized in smart phone, smart TV for swiping screen or volume control. For gesture recognition, robust hand detection is important and necessary but it is challenging because hand shape is complex and hard to be detected in cluttered background, variant illumination. In this paper, we propose efficient hand detection algorithm for detecting pointing hand for recognition of place where user pointed. To minimize false detections, ROIs are generated within the compact search region using skin color detection result. The ROIs are verified by HOG-SVM and pointing direction is computed by both detection results of head-shoulder and hand. In experiment, it is shown that proposed method shows good performance for hand detection.

지능형 로봇 연구 분야에 있어, 손을 이용한 제스처 인식은 매우 중요한 연구 분야로 간주 되고 있으며, 스마트 폰, 스마트 TV 등에 상용화 되어왔다. 제스처 인식에 있어, 강인한 손 검출 기술을 필수적인데, 손의 모양이 일정치 않고, 복잡한 배경이나 조명변화 아래서는 손 검출이 쉽지 않다는 어려움이 있다. 본 논문은 실내 환경에서 사용자가 가리키는 방향을 인식하기 위한 손 검출 알고리즘을 제안한다. 손 검출에 대한 오검출을 최대한 줄이기 위해, 머리-어깨 검출 결과를 기반으로 손 검색 영역을 한정시키고, 피부색을 이용해 최소한의 후보군들을 발생시켜, HOG-SVM을 이용하여 손을 검출하였다. 그리고 머리-어깨, 손 검출 결과를 통해 팔의 방향 각도를 추정하였다. 제안된 방법은 실제 실내 환경에서 추출된 영상을 통해 실험을 진행하였고, 강인한 성능을 확인하였다.

Keywords

References

  1. J. A. Zondag, T. Gritti, and V. Jeanne, "Practical study on real-time hand detection," in Proc. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1-8, 2009
  2. J. Zaletelj, J. Perhavc, and J. Tasic, "Vision-based human-computer interface using hand gestures," Image Analysis for Multimedia Interactive Services,WIAMIS'07. Eighth International Workshop on, pp. 41-41, Jun. 2007.
  3. J. Wen and Y. Zhan, "Vision-based two hand detection and tracking," in Proc. 2nd International Conference on Interaction Sciences Information Technology, Culture and Human - ICIS, pp. 1253-1258, 2009.
  4. P. K. Pisharady, P. Vadakkepat, and A. P. Loh, "Attention based detection and recognition of hand postures against complex backgrounds," International Journal of Computer Vision, vol. 101, no. 3, pp. 403-419, Aug. 2012.
  5. Y. Y. Pang, N. A. Ismail, and P. L. S. Gilbert, "A real time vision-based hand gesture interaction," in Proc. 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, pp. 237-242, 2010.
  6. J. Wen and Y. Zhan, "Vision-based two hand detection and tracking," in Proc. 2nd International Conference on Interaction Sciences Information Technology, Culture and Human - ICIS, pp. 1253- 1258, 2009.
  7. Y. Fang, K. Wang, J. Cheng, and H. Lu, "A real- time hand gesture recognition method," in Proc. IEEE International Conference on Multimedia and Expo, pp. 995-998, 2007.
  8. M. Kolsch and M. Turk, "Robust hand detection.," in Proc. IEEE International Conference on Automatic Face and Gesture Recognition, 2004.
  9. C. Wang and K. Wang, "Hand posture recognition using Adaboost with SIFT for human robot interaction," in Recent progress in robotics: viable robotic service to human. Springer Berlin Heidelberg, pp. 317-329, 2008.
  10. S. Y. Cheng and M. M. Trivedi, "Vision-based infotainment user determination by hand recognition for driver assistance," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 759-764, Sep. 2010. https://doi.org/10.1109/TITS.2010.2049354
  11. E. Ohn-Bar and M. Trivedi, "In-vehicle hand activity recognition using integration of regions," in Proc. IEEE International Intelligent Vehicles Symposium, pp.1034-1039, June, 2013.
  12. Y. Zhao, Z. Song, and X. Wu, "Hand detection using multi-resolution HOG features," in Proc. IEEE International Conference on Robotics and Biomimetics, pp. 1715-1720, Dec. 2012.
  13. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
  14. V. Cherkassky and F. Mulier, "Support -vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  15. P. Peer, F. Solina, "An Automatic Human Face Detection Method, " in Proc. the 4th Computer Vision Winter Workshop, Rastenfeld, Austria, 1999.
  16. J. Kim, J. Baek and E. Kim, "A part-based rotational invariant hand detection," n Proc. International Conference on Fuzzy Theory and I ts Application, Dec. 2013.

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