Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Improvement Method of Tracking Speed for Color Object using Kalman Filter and SURF

SURF(Speeded Up Robust Features)와 Kalman Filter를 이용한 컬러 객체 추적 속도 향상 방법

  • 이희재 (가톨릭대학교 디지털미디어학과 미디어공학전공) ;
  • 이상국 (가톨릭대학교 디지털미디어학부)
  • Received : 2011.08.24
  • Accepted : 2012.01.12
  • Published : 2012.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

As an important part of the Computer Vision, the object recognition and tracking function has infinite possibilities range from motion recognition to aerospace applications. One of methods to improve accuracy of the object recognition, are uses colors which have robustness of orientation, scale and occlusion. Computational cost for extracting features can be reduced by using color. Also, for fast object recognition, predicting the location of the object recognition in a smaller area is more effective than lowering accuracy of the algorithm. In this paper, we propose a method that uses SURF descriptors which applied with color model for improving recognition accuracy and combines with Kalman filter which is Motion estimation algorithm for fast object tracking. As a result, the proposed method classified objects which have same patterns with different colors and showed fast tracking results by performing recognition in ROI which estimates future motion of an object.

객체 인식(recognition)과 추적(tracking)은 컴퓨터 비전의 중요 분야로써 작게는 동작 인식으로부터 크게는 우주 항공까지 그 활용 가능성이 무궁무진하다. 객체 인식의 정확도를 향상시키는 방법 중 하나는 회전, 스케일 그리고 가려짐에 강건한 컬러를 이용하는 것이다. 컬러를 이용함으로써 더 많은 특징점들을 추출하기 위한 계산 비용을 감소시킬 수 있다. 또한, 빠른 객체 인식을 위해 알고리즘의 정확도를 낮추는 것보다 객체의 위치를 예측하고 좀 더 작은 영역에서 인식을 수행하는 것이 더욱 효과적이다. 본 논문은, 인식 정확도를 향상시키기 위해 대표적인 객체 인식 알고리즘인 SURF와 컬러모델을 적용한 기술자(descriptor)를 사용하고, 움직임 예측 알고리즘인 Kalman filter를 결합하여 빠른 객체 추적 방법을 제안한다. 그 결과, 제안하는 방법은 다른 컬러를 갖는 같은 패턴의 객체들을 구분하고, 객체의 향후 움직임을 미리 예측한 관심영역(ROI)에서 인식을 수행함으로써 빠른 추적 결과를 보였다.

Keywords

References

  1. F. Zhou, H.B. Duh, and M. Billinghurst, "Trends in Augmented Reality Tracking, Interaction and Display: A Review of Ten Years of ISMAR," IEEE International Symposium on Mixed and Augmented Reality, pp. 193-202, 2008.
  2. Sang-Goog Lee, "Survey on Mixed Reality R&D," Journal of the Korea Computer Graphics Society, Vol.13, No.2, pp.1-15, 2007.
  3. C. Harris and M. Stephens, "A Combined Corner and Edge Detector," Proc. Alvey Vision Conf., pp. 147-151, 1998.
  4. T. Lindeberg, "Feature Detection with Automatic Scale Selection, " International Journal of Computer Vision, Vol.30, No.3, pp. 79-116, 1998. https://doi.org/10.1023/A:1008045108935
  5. L. Kitchen and A. Rosenfeld, "Gray Level Corner Detection," Pattern Recognition Letters, Vol. 1, Issue 2, pp. 95-102, 1982. https://doi.org/10.1016/0167-8655(82)90020-4
  6. S.M. Smith and J.M. Brady, "SUSAN-A New Approach to Low Level Image Processing," International J ournal of Computer Vision, Vol. 23, No. 1, pp. 45-78, 1997. https://doi.org/10.1023/A:1007963824710
  7. J. Canny, "A Computational Approach to Edge Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851
  8. D. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. of the International Conference on Computer Vision, pp. 1150-1157, 1999.
  9. D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, 60(2), pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  10. M. Brown and D. Lowe. "Invariant Features from Interest Point Groups," In Proceedings of the 13th British Machine Vision Conference, pp. 253-262, 2002.
  11. H. Bay and T. Tuytelaars, and L.V. Gool, "SURF: Speeded Up Robust Features," ECCV, Vol. 3951, pp. 404-417, 2006.
  12. H. Bay, A. Ess, T. Tuytelaars, and Luc Van Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, Vol. 110, Issue 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  13. Luo Juan, Sungsik Shin, Hvun Ju Park, and Ou-Bong Gwun, "Stitching for Panorama based on SURF and Multi-band Blending," Journal of Korea Multimedia Society, Vol.14, No.2, pp. 201-209, 2011. https://doi.org/10.9717/kmms.2011.14.2.201
  14. A.E. Abdel-Hakim and A.A. Farag, "CSIFT: A SIFT Descriptor with Color Invariant Characteristics," Computer Vision and Pattern Recognition, Vol.2. pp. 1978-1983, 2006.
  15. A. Bosch, A. Zisserman, and X. Muoz, "Scene Classification using a Hybrid Generative/ Discriminative Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 30, No. 4, pp. 712-727, 2008. https://doi.org/10.1109/TPAMI.2007.70716
  16. P. Fan, A. Men, M. Chen, and B. Yang, "COLOR-SURF : A Surf Descriptor with Local Kernel Color Histograms," IEEE International Conference on Network Infrastructure and Digital Content, pp. 726-730, 2009.
  17. T. Gevers and AW.M. Smeulders, "Color Based Object Recognition," Pattern Recognition, Vol.32, No.3, pp. 453-464, 1999. https://doi.org/10.1016/S0031-3203(98)00036-3
  18. Greg Welch and Gary Bishop, "An Introduction to the Kalman Filter," SIGGRAPH 2001 Course Notes, 2001.
  19. Zhigang Bing et al., "Research of Tracking Models Based on SURF," First International Conference on Pervasive Computing, Signal Processing and Applications, pp. 517-520, 2010.
  20. P. Binoy and P.R. Anurenjan, "SURF Based Robust Video Stabilization using Kalman Filter," International Conferance on Technological Trends, pp. 1-6, 2010.
  21. Y. Ke and R. Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors," Proc. Computer Vision and Pattern Recognition Conf, pp. 511-517, 2004.

Cited by

  1. Object-Tracking System Using Combination of CAMshift and Kalman filter Algorithm vol.16, pp.5, 2013, https://doi.org/10.9717/kmms.2013.16.5.619
  2. Face Tracking System Using Updated Skin Color vol.18, pp.5, 2015, https://doi.org/10.9717/kmms.2015.18.5.610