DOI QR코드

DOI QR Code

핸드 제스처를 인식하는 손동작 추적

Hand Movement Tracking and Recognizing Hand Gestures

  • 박광채 (조선대학교 전자공학과) ;
  • 배철수 (관동대학교 정보통신공학과)
  • 투고 : 2013.07.23
  • 심사 : 2013.08.07
  • 발행 : 2013.08.31

초록

본 논문은 핸드 제스쳐에 의해 증강현실 내의 가상 객체 제어기술로, HOG기반의 핸드 제스쳐 인식을 제안하고 있다. 인식을 위한 특징점들은 HOG불럭들에 의하여 결정되며, 크기가 다른 여러 불럭들을 시험하여 가장 적절한 불럭구성을 결정하며, AdaBoostSVM기법을 사용하여 분류 목적에 가장 적절한 불럭들을 추출한다. 실험 결과 핸드 제스쳐 인식률은 94% 이었다.

This paper introduces an Augmented Reality system recognizing hand gestures and shows results of the evaluation. The system's user can interact with artificial objects and manipulate their position and motions simply by his hand gestures. Hand gesture recognition is based on Histograms of Oriented Gradients (HOG). Salient features of human hand appearance are detected by HOG blocks. Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification multiclass AdaBoostSVM algorithm is applied. Evaluated recognition rate of the algorithm is 94.0%.

키워드

참고문헌

  1. Hiroyuki Arai, "Measurement of mobile antenna systems," Artech House, 2001.
  2. Gary E. Evans, "Antenna measurement techniques," Artech House, 1990.
  3. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection", Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1063-6919, 2005.
  4. Robert E. Schapire and Yoram Singer, "Improved boosting algorithms using confidence-rated predictions," Machine Learning, vol. 37, no. 3, pp. 297-336, Dec 1999. https://doi.org/10.1023/A:1007614523901
  5. V. Vapnik, S. Golowich, and A. Smola. "Support vector method for function approximation, regression estimation, and signal processing", In M. Mozer, M. Jordan, and T. Petsche, editors, Proc. Advances in Neural Information Processing Systems 9, pp. 281- 287, Cambridge, MA, 1997. MIT Press.
  6. Zhu, Q.; Avidan, S.; Yeh, M-C; Cheng, K-W, "Fast Human Detection Using a Cascade of Histograms of Oriented Gradients", Proc. IEEE Computer Society Conference on Computer vision and Pattern Recognition, ISSN: 1063-6919, vol. 2, pp. 1491-1498, June 2006.
  7. G. D. Abowd et. al., "Teaching and Learning as Multimedia Authoring: The Classroom 2000 Project," ACM Multimedia, pp. 187-198, 2000.
  8. S. G. Deshpande & J.-N. Hwang, "A Real-time Interactive Virtual Classroom Multimedia Distance Learning System," IEEE Trans on Multimedia, vol. 3, no. 4, pp. 432-444, 2001. https://doi.org/10.1109/6046.966115
  9. D. Phung, S. Venkatesh & C. Dorai, "High Level Segmentation of Instructional Videos Based on Content Density," ACM Multimedia, 2002.
  10. Q. Liu, Y. Rui, A. Gupta & J. J. Cadiz, "Automatic Camera Management for Lecture Room Environment," Int. Conf. on Human Fectirs in Computing Systems, 2001.
  11. T. F. S. -Mahmood, "Indexing for topics in videos using foils," Int. Conf. CVPR, pp. 312-319, 2000.
  12. J. Martin & J. B. Durand, "Automatic Gestures Recognition Using Hidden Markov Models," Int. Conf. Automatic Face and Gesture Recognition, 2000.
  13. C. W. Ngo, T. C. Pong & T. S. Huang, "Detection of Slide Transition for Topic Indexing," Int. Conf. on Multimedia Expo, 2002.