DOI QR코드

DOI QR Code

Object Tracking using Adaptive Template Matching

  • Chantara, Wisarut (Gwangju Institute of Science and Technology (GIST)) ;
  • Mun, Ji-Hun (Gwangju Institute of Science and Technology (GIST)) ;
  • Shin, Dong-Won (Gwangju Institute of Science and Technology (GIST)) ;
  • Ho, Yo-Sung (Gwangju Institute of Science and Technology (GIST))
  • 투고 : 2014.04.05
  • 심사 : 2014.11.19
  • 발행 : 2015.02.28

초록

Template matching is used for many applications in image processing. One of the most researched topics is object tracking. Normalized Cross Correlation (NCC) is the basic statistical approach to match images. NCC is used for template matching or pattern recognition. A template can be considered from a reference image, and an image from a scene can be considered as a source image. The objective is to establish the correspondence between the reference and source images. The matching gives a measure of the degree of similarity between the image and the template. A problem with NCC is its high computational cost and occasional mismatching. To deal with this problem, this paper presents an algorithm based on the Sum of Squared Difference (SSD) and an adaptive template matching to enhance the quality of the template matching in object tracking. The SSD provides low computational cost, while the adaptive template matching increases the accuracy matching. The experimental results showed that the proposed algorithm is quite efficient for image matching. The effectiveness of this method is demonstrated by several situations in the results section.

키워드

참고문헌

  1. N. Prabhakar, V. Vaithiyananthan, A.P. Sharma, A. Singh, and P. Singhal, "Object Tracking Using Frame Differencing and Template Matching," Research Journal of Applied Sciences, Engineering and Technology, pp. 5497-5501, Dec. 2012.
  2. A.Yilmaz, O.Javed, and M.Shah, "Object Tracking: A Survey," ACM Computing Surveys, vol. 38(4), Article 13, pp.1-45, Dec. 2006. https://doi.org/10.1145/1132952.1132953
  3. D.Mao, Y.Y. Cao, J.H. Xu, and K. Li, "Object tracking integrating template matching and mean shift algorithm," Multimedia Technology (ICMT), 2011 International Conference on, pp. 3583-3586, Jul. 2011.
  4. J.H. Choi, K.H. Lee, K.C. Cha, J.S. Kwon, D.W. Kim, and H.K. Song, "Vehicle Tracking using Template Matching based on Feature Points," Information Reuse and Integration, 2006 IEEE International Conference on, pp. 573-577, Sep. 2006.
  5. S. Sahani, G. Adhikari, and B. Das, "A fast template matching algorithm for aerial object tracking," Image Information Processing (ICIIP), 2011 International Conference on, pp. 1-6, Nov. 2011.
  6. H.T. Nguyen, M. Worring, and R.V.D. Boomgaad, "Occlusion robust adaptive template tracking," Proceedings. Eighth IEEE International conference on Computer Vision (ICCV), pp. 678-683, 2001.
  7. J. P. Lewis, "Fast template matching," Vis. Inf., pp. 120-123, 1995.
  8. F. Alsaade and Y.M. Fouda, "Template Matching based on SAD and Pyramid," International Journal of Computer Science and Information Security (IJCSIS), vol. 10 no.4, pp. 11-16, Apr. 2012.
  9. J. Shi and C.Tomisto, "Good feature to track," Proceedings of IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp. 593-600, Jun. 1994.
  10. W. K. Pratt, "Correlation techniques of image registration," IEEE Trans. On Aerospace and Electronic Systems, vol. AES-10, pp. 353-358, May 1974. https://doi.org/10.1109/TAES.1974.307828
  11. F. Essannouni, R. Oulad Haj Thami, D. Aboutajdine, and A. Salam, "Adjustable SAD matching algorithm using frequency domain" Journal of Real-Time Image Processing, vol. 1, no. 4, pp. 257-265, Jul. 2007. https://doi.org/10.1007/s11554-007-0026-0
  12. Y. Hel-Or and H. Hel-Or, "Real-time pattern matching using projection kernels," IEEE Trans. PAMI, vol. 27, no. 9, pp. 1430-1445, Sep. 2002.
  13. S. Wei and S. Lai, "Fast template matching based on normalized cross correlation with adaptive multilevel winner update" IEEE Trans. Image processing, vol. 17, No. 11, pp. 2227-2235, Nov. 2008. https://doi.org/10.1109/TIP.2008.2004615
  14. N.P. Papanikolopoulos, "Selection of Features and Evaluation of Visual Measurements During Robotic Visual Servoing Tasks," Journal of Intelligent and Robotic System, vol.13 (3), pp. 279-304, Jul. 1995. https://doi.org/10.1007/BF01424011
  15. P. Anandan, "A computational framework and an algorithm for the measurement of visual motion," International Journal of Computer Vision, vol. 2 (3), pp. 283-310, Jan. 1989. https://doi.org/10.1007/BF00158167
  16. A. Singh and P. Allen, "Image flow computation: an estimation-theoretic framework and a unified perspective," Computer Vision Graphics and Image Processing: Image Understanding, vol. 56 (2), pp. 152-177, Sep. 1992.
  17. G. Hager and P. Belhumeur, "Real-time tracking of image regions with changes in geometry and illumination," Proceedings of IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp. 403-410, Jun. 1996.
  18. Y. Wu, J. Lim, and M.H. Yang, "Online Object Tracking: A Benchmark," Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, pp. 2411-2418, Jun. 2013.

피인용 문헌

  1. Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror vol.17, pp.11, 2017, https://doi.org/10.3390/s17112483
  2. Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path vol.17, pp.2, 2017, https://doi.org/10.3390/s17020337
  3. A System for Real-Time Syringe Classification and Volume Measurement Using a Combination of Image Processing and Artificial Neural Networks pp.1939-8042, 2018, https://doi.org/10.1007/s12247-018-9358-5
  4. Template Matching for Wide-Baseline Panoramic Images from a Vehicle-Borne Multi-Camera Rig vol.7, pp.7, 2018, https://doi.org/10.3390/ijgi7070236