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http://dx.doi.org/10.3837/tiis.2017.01.024

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)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.1, 2017 , pp. 451-465 More about this Journal
Abstract
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.
Keywords
Extreme learning machine; visual tracking; overall output weights; random Haar-like features; adaptive learning rate;
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1 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.
2 A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," ACM Computing Surveys, vol. 38, no. 4, 2006.
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 H. Grabner, C. Leistner, and H. Bischof, "Semi-supervised on-line boosting for robust tracking," Computer Vision-ECCV 2008, pp. 234-247: Springer, 2008.
7 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.
8 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.
9 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.   DOI
10 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.   DOI
11 H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting," British Machine Vision Conference, pp. 47-56, 2006.
12 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.   DOI
13 G. Huang, G.-B. Huang, S. Song, K. You, "Trends in Extreme Learning Machines: A Review," Neural Networks, vol. 61, pp. 32-48, 2015.   DOI
14 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.   DOI
15 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.   DOI
16 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.
17 G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006.   DOI
18 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.
19 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.   DOI
20 G.-B. Huang, L. Chen, "Convex incremental extreme learning machine," Neurocomputing, vol. 70, pp.3056-3062, 2007.   DOI
21 M.-B. Li, G.-B. Huang, P. Saratchandran, N. Sundararajan, "Fully complex extreme learning machine," Neurocomputing, vol. 68, pp. 306C314, 2005.   DOI
22 Y. Lan, Y. C. Soh, and G.-B. Huang, "Two-stage extreme learning machine for regression," Neurocomputing, vol. 73, pp. 3028C3038, 2010.   DOI
23 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.
24 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.   DOI
25 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.   DOI
26 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.   DOI
27 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.   DOI
28 C. R. Rao and S. K. Mitra, "Generalized inverse of matrices and its applications," New York: Wiley, pp. 601-620, 1971.
29 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.
30 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.
31 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.