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Vision-Based Indoor Object Tracking Using Mean-Shift Algorithm

평균 이동 알고리즘을 이용한 영상기반 실내 물체 추적

  • 김종훈 (부산대학교 항공우주공학과) ;
  • 조겸래 (부산대학교 항공우주공학과) ;
  • 이대우 (부산대학교 항공우주공학과)
  • Published : 2006.07.01

Abstract

In this paper, we present tracking algorithm for the indoor moving object. We research passive method using a camera and image processing. It had been researched to use dynamic based estimators, such as Kalman Filter, Extended Kalman Filter and Particle Filter for tracking moving object. These algorithm have a good performance on real-time tracking, but they have a limit. If the shape of object is changed or object is located on complex background, they will fail to track them. This problem will need the complicated image processing algorithm. Finally, a large algorithm is made from integration of dynamic based estimator and image processing algorithm. For eliminating this inefficiency problem, image based estimator, Mean-shift Algorithm is suggested. This algorithm is implemented by color histogram. In other words, it decide coordinate of object's center from using probability density of histogram in image. Although shape is changed, this is not disturbed by complex background and can track object. This paper shows the results in real camera system, and decides 3D coordinate using the data from mean-shift algorithm and relationship of real frame and camera frame.

Keywords

References

  1. 김종훈, 조겸래, 이대우, 노민식 '인공신경망을 이용한 헤드트레커 실험 구현' 춘계학술발표회, 한국항공우주학회, 2005. 4
  2. Y. Boykoc and D. Huttenlocher, 'Adaptive bayesian recognition in tracking rigid objects,' proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 679-704, 2000
  3. R. Rosales and S. Sclaroff, '3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions,' Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 117-123, 1999 https://doi.org/10.1109/CVPR.1999.784618
  4. M. Isard and A. Blake, 'Condensation-conditional density propagation for visual trakcing,' Int'l J. Computer Vision, vol. 29, no. 1, 1998
  5. D. Comaniciu, V. Ramesh. 'Robust detection and tracking of human faces with an active camera,' IEEE Workshop on Visual Suveillance, pp. 11-18, 2000 https://doi.org/10.1109/VS.2000.856853
  6. D. Comaniciu and P. Meer, 'Mean shift analysis and applications,' IEEE Conference on Computer Vision and Pattern Recognition, pp. 1197-1203, 1999 https://doi.org/10.1109/ICCV.1999.790416
  7. D. Comaniciu, V. Ramesh, and P. Meer, 'Real-time tracking of non-rigid objects using mean shift,' Conference on Computer Vision and Pattern Recognition, 2000 https://doi.org/10.1109/CVPR.2000.854761
  8. G. B. Chattetji, P. K. Menon, and S. Sridhar, 'GPS/Machine vision navigation system for aircraft,' IEEE Transactions on Aerospace and Electronic Systems, vol. 33, no. 3, pp https://doi.org/10.1109/7.599326

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