Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
권호 표지
DOI QR코드

DOI QR Code

Hand Tracking based on CamShift using Motion History Image

운동 히스토리 영상을 활용한 CamShift 기반 손 추적 기법

  • Gil, Jong In (Dept. of Computer & Communications Engineering Kangwon University) ;
  • Kim, Mina (Dept. of Computer & Communications Engineering Kangwon University) ;
  • Whang, Whankyu (Dept. of Computer & Communications Engineering Kangwon University) ;
  • Kim, Manbae (Dept. of Computer & Communications Engineering Kangwon University)
  • 길종인 (강원대학교 컴퓨터정보통신공학과) ;
  • 김미나 (강원대학교 컴퓨터정보통신공학과) ;
  • 황환규 (강원대학교 컴퓨터정보통신공학과) ;
  • 김만배 (강원대학교 컴퓨터정보통신공학과)
  • Received : 2017.01.11
  • Accepted : 2017.03.02
  • Published : 2017.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose hand tracking system combined with color and motion information. Most of hand detection and tracking systems are performed by modeling skin color. However, in this approach, since it is highly influenced by light or surrounding objects, accurate values cannot be derived constantly. Also, depending on the skin color, hand tracking may be interrupted by not only the hand but also the background with a color similar to that of the face and skin. Therefore, we design the hand tracking that can effectively track a hand by using motion history image(MHI) and combining it with CamShift. The proposed system is implemented based on C/C++, and the experiments proved that the proposed method shows stable and excellent performance.

본 논문에서는 컬러와 운동 정보를 혼합한 손 추적 시스템을 제안하고자 한다. 손의 검출 및 추적은 많은 경우 피부색을 모델링하여 검출을 하는 방식을 사용한다. 하지만 이와 같은 방법으로는 빛이나 주변 사물에 의해 영향을 많이 받기 때문에 정확한 값을 일정하게 도출해 낼 수 없었다. 또한, 피부색에 의존하므로, 손뿐만 아니라 얼굴 및 비부 색과 비슷한 색을 갖는 배경 등에 의해 추적이 방해받을 수 있다. 이에 본 논문은 운동 히스토리 기법(MHI)을 이용하여 움직임을 파악한 후 이를 CamShift와 결합함으로서, 효과적으로 추적할 수 있도록 설계하였다. 제안된 시스템은 C/C++을 기반으로 구현하였으며, 실험에서 제안 방법이 안정적이고 우수한 성능을 보여줌을 증명하였다.

Keywords

References

  1. O. Kainz and F. Jakab, "Approach to Hand Tracking and Gesture Recognition Based on Depth-Sensing Cameras and EMG Monitoring," Acta Informatica Pragensia, vol. 3, no. 1, pp. 104-112, 2014. https://doi.org/10.18267/j.aip.38
  2. Y. Sato, Y. Kobayashi and H. Koike, "Fast Tracking of Hands and Fingertips in Infrared Images for Augmented Desk Interface," IEEE International Conference on Automatic Face and Gesture Recognition, pp. 462-467, 2000.
  3. P. Brasnett, L. Mihaylova, N. Canagarajah and D. Bull, "Particle filtering with multiple cues for object tracking in video sequences," Proceedings of SPIE 5685, Image and Video Communications and Processing, pp. 430-441, Jan. 2005.
  4. T. Zhang and S. Fei, "Target tracking based on particle filter algorithm with multiple cues fusion," Proceedings of the 26th Chinese Control and Decision Conference (2014 CCDC), pp. 1660-1665, May 2014.
  5. S.-M. Liang and S.-S Huang, "Fingertip positioning and tracking by fusing multiple cues using particle filtering," Proceedings of 2013 IEEE International Symposium on Consumer Electronics (ISCE), pp. 205-206, June 2013.
  6. H. Zhou, Y. Gao, G. Yuan and R. Ji, "Adaptive multiple cues integration for particle filter tracking," Proceedings of IET International Radar Conference 2015, pp. 483-488, Oct. 2015.
  7. L. Liu, X. Li, Y. Zhao and J. Chen, "A Real-time and Low-cost Hand Tracking System," IEEE International Conference on Consumer Electronics, 2017.
  8. Z. Wen, Z. Peng, X. Deng and S. Li, "Particle filter object tracking based on multiple cues fusion," Procedia Engineering, vol. 15, pp. 1461-1465, 2011. https://doi.org/10.1016/j.proeng.2011.08.271
  9. C. R. Wren, A. Azarbayejani. A. T. Darrel and A. P. Pentland, "Pfinder: Real-time Tracking of the Human Body," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, pp. 780-785, 1997. https://doi.org/10.1109/34.598236
  10. C. Stauffer and W. E. L. Grimson, "Learning Patterns of Activity Using Real Time Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-757, 2000, https://doi.org/10.1109/34.868677
  11. I. Haritaoglu, D. Harwood, L. S. Davis, "W4: Real-time Surveillance of People and Their Activities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, 2000. https://doi.org/10.1109/34.868683
  12. K. Kim, T. H. Chalidabhongse, D. Harwood and L. Davis, "Real-Time Foreground-Background Segmentation Using Codebook Model," Real-Time Imaging, vol. 11, no. 3, pp. 172-185, 2005.
  13. A. Bobick and J. Davis, "The recognition of human movement using temporal templates," IEEE Transactions on Pattern Analysis and Pattern Analysis, Vol 23, No. 3, March 2001.
  14. D. Comaniciu, and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002. https://doi.org/10.1109/34.1000236
  15. G. R. Bradski, "Computer Vision Face Tracking as a Component of a Perceptual User Interface", IEEE Workshop Applications of Computer Vision, pp. 214-219, Oct. 1998.
  16. R. Stolkin, I. Florescu, M. Baron, C. Harrier and B. Kocherov, "Efficient Visual Servoing with the ABCshift Tracking Algorithm," International Conference on Robotics and Automation, pp. 3219-3224, 2008.
  17. Lehmann, E. Leo and G. Casella, Theory of point estimation, Springer Science & Business Media, 2006.