AdaBoost-based Gesture Recognition Using Time Interval Window Applied Global and Local Feature Vectors with Mono Camera

모노 카메라 영상기반 시간 간격 윈도우를 이용한 광역 및 지역 특징 벡터 적용 AdaBoost기반 제스처 인식

  • Hwang, Seung-Jun (Department of Electronics and Information Engineering, Korea Aerospace University) ;
  • Ko, Ha-Yoon (Department of Electronics and Information Engineering, Korea Aerospace University) ;
  • Baek, Joong-Hwan (Department of Electronics and Information Engineering, Korea Aerospace University)
  • Received : 2018.01.15
  • Accepted : 2018.01.24
  • Published : 2018.03.28


Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.


Grant : Development of Interactive VR Player and Service with Space Media Convergence

Supported by : Ministry of Science, ICT and Future Planning, GRRC


  1. L. Chen, H. Wei, and J. Ferryman. "A survey of human motion analysis using depth imagery," Pattern Recognition Letters, vol. 34, no. 15, pp. 1995-2006, Nov. 2013.
  2. K. Lee, Y. Shin, Y. Lee, and S. Seol, "A Study on User Interface and Control Method of Web-based Remote Control Platform," Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, ISSN:2383-5287, vol. 7, no.6, pp. 827-837, June 2017.
  3. K. J. Lee, "A Study on Gesture Recognition using Edge Orientation Histogram and HMM," Journal of the Korea Institute of Information and Communication Engineering, vol. 15, no. 12, pp. 2647-2654, Dec. 2011.
  4. H. Duan and Y. Luo. "A Gestures Trajectory Recognition Method Based on DTW," in Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering, pp. 364-366, 2013.
  5. S. J. Hwang, et al. "Human Body Tracking and Pose Estimation Using Modified Camshift Algorithm," Journal of Software Engineering and Applications, vol. 6. no. 5B, pp. 37-42, May 2013.
  6. S, Jia. "A study of adaboost in 3D gesture recognition," Department of Computer Science, University of Toronto, Technical Report, 2003.
  7. Patsadu, Orasa, C. Nukoolkit, and B. Watanapa. "Human gesture recognition using Kinect camera," Computer Science and Software Engineering, 2012 International Joint Conference on. IEEE, Bangkok, Thailand, pp. 28-32, 2012.
  8. Arici, Tarik, et al. "Robust gesture recognition using feature pre-processing and weighted dynamic time warping," Multimedia Tools and Applications, vol. 72, no. 3, pp 3045-3062, Oct. 2013
  9. Takimoto, Hironori, J. Lee, and A. Kanagawa. "A robust gesture recognition using depth data," International Journal of Machine Learning and Computing, vol. 3, no. 2, pp 245-249, Apr. 2013.
  10. Freund, Yoav, R. Schapire, and N. Abe. "A short introduction to boosting," Journal-Japanese Society For Artificial Intelligence vol. 14, no. 5, pp 771-780, Sep. 1999.
  11. J. Zhu, S. Rosset, H. Zou and T. Hastie. "Multi-class adaboost," Technical Report 430, Department of Statistics, University of Michigan, 2009.
  12. Hoffman, Michael, P. Varcholik, and Joseph J. LaViola. "Breaking the status quo: Improving 3d gesture recognition with spatially convenient input devices," Virtual Reality Conference, Waltham, MA, USA, pp. 59-66, Mar. 2010
  13. S. J. Hwang, G. P. Ahn, S. J. Park and J. H. Baek. "AdaBoost-Based Gesture Recognition Using Time Interval Trajectory Features," Journal of Advanced Navigation Technology, vol. 17, no. 2, pp 247-254, Apr. 2013.