KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권1호
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  • Pages.451-465
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  • 2017
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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A novel visual tracking system with adaptive incremental extreme learning machine

  • Wang, Zhihui (National Key Laboratory, Hisense Company Limited) ;
  • Yoon, Sook (Department of Multimedia Engineering, Mokpo National University) ;
  • Park, Dong Sun (Division of Electronics Engineering, Chonbuk National University)
  • 투고 : 2016.03.07
  • 심사 : 2016.12.04
  • 발행 : 2017.01.31
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초록

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

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과제정보

연구 과제 주관 기관 : National Research Foundation of Korea(NRF)

참고문헌

  1. A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Computing Surveys, vol. 38, no. 4, 2006.
  2. H. Yang, L. Shao, F. Zheng, L. Wang, and Z. Song, "Recent advances and trends in visual tracking: A review," Neurocomputing, vol. 74, no. 18, pp. 3823-3831, 2011. https://doi.org/10.1016/j.neucom.2011.07.024
  3. Y. Su, Q. Zhao, L. Zhao, and D. Gu, "Abrupt motion tracking using a visual saliency embedded particle filter," Pattern Recognition, vol. 47, no. 5, pp. 1826-1834, 2014. https://doi.org/10.1016/j.patcog.2013.11.028
  4. C. H. Hsia, Y. J. Liou, and J. S. Chiang, "Directional Prediction CamShift algorithm based on Adaptive Search Pattern for moving object tracking," Journal of Real-Time Image Processing, DOI 10.1007/s11554-013-0382-x, 2015.
  5. H. Grabner, C. Leistner, and H. Bischof, "Semi-supervised on-line boosting for robust tracking," Computer Vision-ECCV 2008, pp. 234-247: Springer, 2008.
  6. C. Migniot, and F. Ababsa, "Hybrid 3DC2D human tracking in a top view," Journal of Real-Time Image Processing, Vol 11, issue 4, pp. 769-784, 2015.
  7. T. Bai, and Y. F. Li, "Robust visual tracking with structured sparse representation appearance model," Pattern Recognition, vol. 45, no. 6, pp. 2390-2404, 2012. https://doi.org/10.1016/j.patcog.2011.12.004
  8. R. V. Babu, S. Suresh, and A. Makur, "Online adaptive radial basis function networks for robust object tracking," Computer Vision and Image Understanding, vol. 114, no. 3, pp. 297-310, 2010. https://doi.org/10.1016/j.cviu.2009.10.004
  9. D. Wang, H. Lu, and M.-H. Yang, "Least Soft-thresold Squares Tracking," in Proc. of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, 2013.
  10. H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting," British Machine Vision Conference, pp. 47-56, 2006.
  11. 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. 1-3, pp. 125-141, 2008. https://doi.org/10.1007/s11263-007-0075-7
  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, 2010. https://doi.org/10.1109/TPAMI.2011.239
  13. B. Babenko, M.-H. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619-1632, 2011. https://doi.org/10.1109/TPAMI.2010.226
  14. S. He, Q. Yang, R. W. Lau, J. Wang, and M.-H. Yang, "Visual tracking via locality sensitive histograms," in Proc. of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2427-2434, Portland, Oregon, USA, 2013.
  15. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  16. G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, "Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks," in Proc. of the International Joint Conference on Neural Networks (IJCNN 2004), Budapest,Hungary, pp. 25-29, Jul. 2004.
  17. G. Huang, G.-B. Huang, S. Song, K. You, "Trends in Extreme Learning Machines: A Review," Neural Networks, vol. 61, pp. 32-48, 2015. https://doi.org/10.1016/j.neunet.2014.10.001
  18. L. L. C. Kasun, H. Zhou, G. -B. Huang, C. M. Vong, "Extreme Learning Machines," IEEE Intelligent System, vol. 28, no. 6, pp. 30-59, 2013. https://doi.org/10.1109/MIS.2013.140
  19. G.-B. Huang, L. Chen, "Convex incremental extreme learning machine," Neurocomputing, vol. 70, pp.3056-3062, 2007. https://doi.org/10.1016/j.neucom.2007.02.009
  20. M.-B. Li, G.-B. Huang, P. Saratchandran, N. Sundararajan, "Fully complex extreme learning machine," Neurocomputing, vol. 68, pp. 306C314, 2005. https://doi.org/10.1016/j.neucom.2005.03.002
  21. Y. Lan, Y. C. Soh, and G.-B. Huang, "Two-stage extreme learning machine for regression," Neurocomputing, vol. 73, pp. 3028C3038, 2010. https://doi.org/10.1016/j.neucom.2010.07.012
  22. G. Huang, S. Song, J. N. D. Gupta, C. Wu, "Semi-supervised and Unsupervised Extreme Learning Machines," IEEE transactions on cybernetics, vol. 44, no. 12, 2405-2417, 2014. https://doi.org/10.1109/TCYB.2014.2307349
  23. L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, "Representational Learning with Extreme Learning Machine for Big Data," IEEE Intelligent System, vol. 28, no. 6, pp. 31-34, 2013. https://doi.org/10.1109/MIS.2013.4
  24. N. Y. Liang, G.-B. Huang, P. Saratchandran, N. Sundararajan, "A fast and accurate online sequential learning algorithm for feedforward networks," IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411-1423, 2006. https://doi.org/10.1109/TNN.2006.880583
  25. R. Collins, Y. Liu, M. Leordeanu, "Online selection of discriminative tracking features," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1631-1643, 2005. https://doi.org/10.1109/TPAMI.2005.205
  26. C. R. Rao and S. K. Mitra, "Generalized inverse of matrices and its applications," New York: Wiley, pp. 601-620, 1971.
  27. C. Bao, Y. Wu, H. Ling, and H. Ji, "Real time robust L1 tracker using accelerated proximal gradient approach," in Proc. of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012.
  28. S. Oron, A. Bar-Hillel, D. Levi, and S. Avidan, "Locally orderless tracking," in Proc. of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012.
  29. Y. Wu, B. Shen, and H. Ling, "Online robust image alignment via iterative convex optimization," in Proc. of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012.
  30. Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark," in Proc. of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411-2418, Portland, Oregon, USA, 2013.
  31. M. Kristan, R. Pugfelder, A. Leonardis, A., et al., "The visual object tracking vot2014 challenge results," in Proc. of the Computer Vision-ECCV 2014Workshops, pp. 191-217, Springer, Zurich, Switzerland, 2014.

피인용 문헌

  1. Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine vol.2019, pp.None, 2017, https://doi.org/10.1155/2019/9142694