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

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

Target Modeling with Color Arrangement for Region-Based Object Tracking

영역 기반 물체 추적에서 색상 배치를 고려한 표적 모델링

  • Kim, Dae-Hwan (School of Electrical Engineering, Korea University) ;
  • Lee, Seung-Jun (School of Electrical Engineering, Korea University) ;
  • Ko, Sung-Jea (School of Electrical Engineering, Korea University)
  • 김대환 (고려대학교 전기전자전파공학부) ;
  • 이승준 (고려대학교 전기전자전파공학부) ;
  • 고성제 (고려대학교 전기전자전파공학부)
  • Received : 2011.04.13
  • Accepted : 2011.11.01
  • Published : 2012.01.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a new class of color histogram model suitable for object tracking. In addition to the pixel count, each bin of the proposed model also contains the spatial mean and the average value of the pixels located at a certain distance from the mean location of the bin. Using the proposed color histogram model, we derive a mean shift procedure using the modified Bhattacharyya distance. Unlike most mean shift based methods, our algorithm performs well even when the object being tracked shares similar colors with the background. Experimental results demonstrate improved tracking performance over existing methods.

본 논문은 물체 추적에 적합한 새로운 형식의 히스토그램 모델을 제안한다. 제안하는 색상 히스토그램은 양자화 된 각 색상요소에 대해 픽셀의 개수뿐만 아니라 평균 위치 정보 그리고 평균 위치로부터 일정하게 떨어진 영역에 속하는 픽셀들의 색상평균값을 포함한다. 또한 제안하는 히스토그램간의 유사도를 나타내기 위하여 Bhattacharyya 거리를 기본으로 새로운 유사도 함수를 정의하고 mean shift 기법에 적용한다. 기존의 mean shift 기반 기법들과는 달리 본 논문에서 제안하는 알고리즘은 물체 주변 배경 영역에 물체와 비슷한 색상이 존재하더라도 강건한 물체 추적이 가능하다. 실험 결과는 기존 기법들과의 비교를 통하여 개선된 추적 결과를 보여준다.

Keywords

References

  1. A. Yilmaz, O Javed, and M Shah, "Object tracking: A survey," ACM Computing Surveys, Vol. 38, no. 4, Dec. 2006.
  2. R. T. Collins, "Mean-shift blob tracking through scale space," In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 2, pp. 234-240, June 2003.
  3. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, no. 5, pp. 564-577, May 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  4. D. Serby, S. Koller, and L. V. Gool, "Probabilistic object tracking using multiple features," In Proc. of IEEE Conf. on International Conference of Pattern Recognition, Vol. 2, pp. 184-187, Aug. 2004.
  5. S. T. Birchfield and S. Rangarajan, "Spatiograms versus histograms for region-based object tracking," In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 2, pp. 1158-1163, June 2005.
  6. A. Yilmaz, X. Li, and M. Shah, "Contour based object tracking with occlusion handling in video acquired using mobile cameras," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 26, no. 11, pp. 1531-1536, Nov. 2004. https://doi.org/10.1109/TPAMI.2004.96
  7. Qingxiong Yang, Ruigang Yang, J. Davis, and D. Nister, "Spatial-Depth Super Resolution for Range Images," In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-8, June 2007.
  8. S. Zhu and A. Yuille, "Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18, no. 9, pp. 884-900, Sep. 1996. https://doi.org/10.1109/34.537343
  9. N. Paragios and R. Deriche, "Geodesic active regions and level set methods for supervised texture segmentation," Int. J. Comput. Vision Vol. 46, no. 3, pp. 223-247, 2002. https://doi.org/10.1023/A:1014080923068
  10. A. Elgammal, R. Duraiswami, D. Harwood, and L. Davis, "Background and foreground modeling using nonparametric kernel density estimation for visual surveillance," Proceedings of IEEE, Vol. 90, no. 7, pp. 1151-1163, July 2002. https://doi.org/10.1109/JPROC.2002.801448
  11. G. Edwards, C. Taylor, and T. Cootes, "Interpreting face images using active appearance models," In International Conference on Face and Gesture Recognition, pp. 300-305, 1998.
  12. B. Horn and B. Schunck, "Determining optical flow," Artific. Intell. Vol. 17, pp. 185-203, 1981. https://doi.org/10.1016/0004-3702(81)90024-2
  13. K. Laws, "Textured image segmentation," PhD thesis, Electrical Engineering University of Southern California, 1980.
  14. Z. Zivkovic and B. Krose, "An EM-like algorithm for color histogram-based object tracking," In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, pp. 798-803, July 2004.
  15. C. O'Conaire, N. E. O'Connor, A. F. Smeaton, "An improved spatiogram similarity measure for robust object localization," in Proc. of IEEE Conf. on Acoustics, Speech and Signal Processing, Vol. 1, pp. 1069-1072, April 2007.
  16. http://www.cvg.rdg.ac.uk/PETS2009/a.html