Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Hierarchical Active Shape Model-based Motion Estimation for Real-time Tracking of Non-rigid Object

계층적 능동형태 모델을 이용한 비정형 객체의 움직임 예측형 실시간 추적

  • Published : 2004.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we proposed a hierarchical ASM for real-time tracking of non-rigid objects. For tracking an object we used ASM for estimating object contour possibly with occlusion. Moreover, to reduce the processing time we used hierarchical approach for real-time tacking. In the next frame we estimated the initial feature point by using Kalman filter. We also added block matching algorithm for increasing accuracy of the estimation. The proposed hierarchical, prediction-based approach was proven to out perform the exiting non-hierarchical, non-prediction methods.

본 논문에서는 비정형 객체를 능동형태 모델을 사용하여 실시간으로 추적하기 위한 방법을 제시하였다. 객체를 추적 할 때, 가려진 부분의 윤곽을 추정해 낼 수 있는 능동형태 모델을 사용하였으며, 비디오의 각 프레임에서 처리과정의 시간을 줄이기 위해서 영상을 계층적으로 분리하여 실시간 처리를 가능하게 하였다 또한 다음 입력영상의 초기 윤곽을 효율적으로 찾기 위해서 칼만필터(Kalman filter)를 사용하여 특징점을 예측하였고, 블록 정합(block matching) 기법을 추가하여 예측 안정성을 향상시켰다. 비 계층적 방법, 비 예측 방법 등과 비교 실험을 통해서 제안된 계층적, 예측형 방식이 수렴속도 증가와 모델링의 정확도에서 모두 개선된 효과를 얻을 수 있음을 확인하였다.

Keywords

References

  1. Y. Boykov, D. P. Huttenlocher, 'Adaptive Bayesian recognition in tracking rigid objects,' Computer Vision and Pattern Recognition, pp. 697-704, Vol.2 2000 https://doi.org/10.1109/CVPR.2000.854942
  2. I. Haritaolu, D. Harwood and L. S. Davis, 'W4: real-time surveillance of people and their activities,' IEEE Trans. on PAMI, 22(8): 809-830, 2000 https://doi.org/10.1109/34.868683
  3. S. J. McKenna, Y. Raja, S. Gong, 'Tracking colour objects using adaptive mixture models,' Image and Vision Computing, vol. 17, pp. 225-231, 1999 https://doi.org/10.1016/S0262-8856(98)00104-8
  4. R. Plankers, P. Fua, 'Tracking and modeling people in video sequences,' Comp. Vision and Image Understanding, vol. 81, pp. 285-302, 2001 https://doi.org/10.1006/cviu.2000.0891
  5. T. J. Cootes, C. J. Taylor, D. H. Cooper, and J. Gragam, 'Training models of shape form sets of examples,' In British Machine Vision Conference, pp.9-18, September 1992
  6. T. F. Cootes, A. Hill, C. J. Taylor C. J., and J. Haslam, 'The use of active shape models for locating structures in medical images,' Information Processing in Medical imaging, pp. 33-47, 1993 https://doi.org/10.1007/BFb0013779
  7. G. Welch, G. Bishop, 'An Introduction to the Kalman Filter,' Technical Report, Department of Comp. Sc. and Engg., Univ. of North Carolina at Chapel Hill, 2002
  8. S. Tanimoto and T. Pavlidis, 'A hierarchical data structure for picture processing,' Comput. Graphics Image Process. vol. 4, pp. 104-119, 1996
  9. A. Koschan, S. Kang, J. Paik, B. Abidi, and M. Abidi, 'Color active shape models for tracking non-rigid objects,' Pattern Recognition Letters, Vol. 24, no. 11, pp. 1751-1765, July 2003 https://doi.org/10.1016/S0167-8655(02)00330-6
  10. A. Baumberg, 'Hierarchical shape fitting using an iterated linear filter,' Image and Vision Computing, vol. 16, pp. 329-335, 1996 https://doi.org/10.1016/S0262-8856(97)00065-6
  11. T. F. Cootes, C. J. Taylor, and A. Lanitis, 'Active shape models: evaluation of a multi-resolution method for improving image search,' Proc. British Machine Vision Conference, pp. 327-336, 1994
  12. S. M. Smith, 'Reviews of Optical Flow, Motion Segmentation, Edge Finding and Corner Finding,' Technical Report, Dept. of Clinical Neurology, Oxford University, 1997
  13. S. Araki, T. Matsuoka, H. Takemura, and N. Yokoya, 'Real-time Tracking of Multiple Moving Objects in Moving Camera Image Sequence,' IEICE Trans. Inf. & Syst. Vol. E83-D, No. 7, 2000
  14. Chin-Chen Chang, Lin-Li Chen, Tung-Shou Chen, 'An improvement. of bottom-up variable-sized block matching technique for video compression,' IEEE Transactions on Consumer Electronics, Vol. 44, No. 4, pp. 1234-1242, 1998 https://doi.org/10.1109/30.735822
  15. A. Murat Tekalp, 'Digital Video Processing,' Prentice hall signal processing series, 1995