KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Size Aware Correlation Filter Tracking with Adaptive Aspect Ratio Estimation

  • Zhu, Xiaozhou (College of Aerospace Science and Engineering, National University of Defense Technology) ;
  • Song, Xin (College of Aerospace Science and Engineering, National University of Defense Technology) ;
  • Chen, Xiaoqian (College of Aerospace Science and Engineering, National University of Defense Technology) ;
  • Bai, Yuzhu (College of Aerospace Science and Engineering, National University of Defense Technology) ;
  • Lu, Huimin (College of Mechatronics and Automation, National University of Defense Technology)
  • Received : 2016.08.07
  • Accepted : 2016.12.28
  • Published : 2017.02.28
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Abstract

Correlation Filter-based Trackers (CFTs) gained popularity recently for their effectiveness and efficiency. To deal with the size changes of the target which may degenerate the tracking performance, scale estimation has been introduced in existing CFTs. However, the variations of the aspect ratio were usually neglected, which also influence the size of the target. In this paper, Size Aware Correlation Filter Trackers (SACFTs) are proposed to deal with this problem. The SACFTs not only determine the translation and scale variations, but also take the aspect ratio changes into consideration, thus a better estimation of the size of the target can be realized, which improves the overall tracking performance. And competing results can be achieved compared with state-of-the-art methods according to the experiments conducted on two large scale datasets.

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References

  1. Y. Wu, J. Lim, and M. H. Yang, "Online object tracking: a benchmark," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411-2418, June 23-28, 2013.
  2. D. A. Ross, J. Lim, R. S. Lin, and M. H. Yang, "Incremental learning for robust visual tracking," International Journal of Computer Vision, Vol. 77, No. 1, pp. 125-141, May, 2008. https://doi.org/10.1007/s11263-007-0075-7
  3. X. Mei, and H. Ling, "Robust visual tracking using ${\ell}1$ minimization," in Proc. of the IEEE International Conference on Computer Vision, pp. 1436-1443, September 29-October 2, 2009.
  4. K. Zhang, L. Zhang, and M. H. Yang, "Real-time compressive tracking," in Proc. of the European Conference on Computer Vision, pp. 864-877, October 7-13, 2012.
  5. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High-speed tracking with kernelized correlation filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 3, pp. 583-596, August, 2014. https://doi.org/10.1109/TPAMI.2014.2345390
  6. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," in Proc. of the European conference on computer vision, pp. 702-715, October 7-13, 2012.
  7. S. Avidan, "Support vector tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 8, pp. 1064-1072, August, 2004. https://doi.org/10.1109/TPAMI.2004.53
  8. S. Hare, A. Saffari, and P. H. S. Torr, "Struck: Structured output tracking with kernels," in Proc. of the IEEE International Conference on Computer Vision, pp. 263-270, November 6-13, 2011.
  9. H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting," in Proc. of the British Machine Vision Conference, pp. 47-56, September 4-7, 2006.
  10. H. Grabner, C. Leistner, and H. Bischof, "Semi-supervised on-line boosting for robust tracking," in Proc. of the European Conference on Computer Vision, pp. 234-247, October 12-18, 2008.
  11. B. Babenko, M. H. Yang, and S. Belongie, "Visual tracking with online multiple instance learning," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 983-990, June 20-25, 2009.
  12. Z. Kalal, K. Mikolajczyk, and J. Matas, "Tracking-learning-detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 7, pp. 1409-1422, August, 2011. https://doi.org/10.1109/TPAMI.2011.239
  13. A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof, "On-line random forests," in Proc. of the International Conference on Computer Vision Workshops, pp. 1393-1400, September 27 - October 4, 2009.
  14. N. Wang, J. Wang, and D. Y. Yeung, "Online robust non-negative dictionary learning for visual tracking," in Proc. of the IEEE International Conference on Computer Vision, pp. 657-664, December 1-8, 2013.
  15. N. Wang, and D. Y. Yeung, "Learning a deep compact image representation for visual tracking," in Proc. of the Neural Information Processing Systems 2013, pp. 809-817, 2013.
  16. A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, "Visual tracking: An experimental survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 7, pp. 1442-1468, July, 2014. https://doi.org/10.1109/TPAMI.2013.230
  17. D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui, "Visual object tracking using adaptive correlation filters," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2544-2550, June 13-18, 2010.
  18. B. Scholkopf, and A. J. Smola, Learning with kernels: Support vector machines, regularization, optimization, and beyond, MIT press, 2002.
  19. Z. Chen, Z. Hong, and D. Tao, "An experimental survey on correlation filter-based tracking," Japanese Circulation Journal, Vol. 53, No. 6025, pp. 68-83, 2015.
  20. N. Dalal, and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 886-893, June 20-25, 2005.
  21. M. Danelljan, F. S. Khan, M. Felsberg, and J. v. d. Weijer, "Adaptive color attributes for real-time visual tracking," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090-1097, June 23-28, 2014.
  22. J. van de Weijer, C. Schmid, J. Verbeek, and D. Larlus, "Learning color names for real-world applications," IEEE Transactions on Image Processing, Vol. 18, No. 7, pp. 1512-1523, July, 2009. https://doi.org/10.1109/TIP.2009.2019809
  23. M. Danelljan, G. Hager, F. Khan, and M. Felsberg, "Convolutional features for correlation filter based visual tracking," in Proc. of the IEEE International Conference on Computer Vision Workshops, pp. 58-66, December 7-13, 2015.
  24. Y. Li, and J. Zhu, "A scale adaptive kernel correlation filter tracker with feature integration," in Proc. of the European Conference on Computer Vision Workshops, pp. 254-265, September 6-7, 2014.
  25. M. Danelljan, G. Hager, F. S. Khan, and M. Felsberg, "Accurate scale estimation for robust visual tracking," in Proc. of the British Machine Vision Conference, September, 2014.
  26. M. Kristan, J. Matas, A. Leonardis, and T. Vojir, "A novel performance evaluation methodology for single-target trackers," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015.
  27. M. Zhang, J. Xing, J. Gao, and X. Shi, "Joint scale-spatial correlation tracking with adaptive rotation estimation," in Proc. of the IEEE International Conference on Computer Vision Workshop, pp. 595-603, December 7-13, 2015.
  28. M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernandez, T. Vojir, G. Hager, G. Nebehay, and R. Pflugfelder, "The visual object tracking vot2015 challenge results," in Proc. of the IEEE International Conference on Computer Vision Workshops, pp. 1-23, December 7-13, 2015.
  29. B. V. K. V. Kumar, J. A. Fernandez, A. Rodriguez, and V. N. Boddeti, "Recent advances in correlation filter theory and application," in Proc. of the SPIE Defense + Security, pp. 909404-909413, May 5, 2014.
  30. H. Nam, and B. Han, "Learning multi-domain convolutional neural networks for visual tracking," CoRR, 2015.
  31. T. Vojir, J. Noskova, and J. Matas, "Robust scale-adaptive mean-shift for tracking," Pattern Recognition Letters, Vol. 49, pp.250-258, November, 2014. https://doi.org/10.1016/j.patrec.2014.03.025