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http://dx.doi.org/10.7471/ikeee.2018.22.3.810

Robust Object Tracking based on Kernelized Correlation Filter with multiple scale scheme  

Yoon, Jun Han (Dept. of Computer Engineering, Seokyeong University)
Kim, Jin Heon (Dept. of Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.22, no.3, 2018 , pp. 810-815 More about this Journal
Abstract
The kernelized correlation filter algorithm yielded meaningful results in accuracy for object tracking. However, because of the use of a fixed size template, we could not cope with the scale change of the tracking object. In this paper, we propose a method to track objects by finding the best scale for each frame using correlation filtering response values in multi-scale using nearest neighbor interpolation and Gaussian normalization. The scale values of the next frame are updated using the optimal scale value of the previous frame and the optimal scale value of the next frame is found again. For the accuracy comparison, the validity of the proposed method is verified by using the VOT2014 data used in the existing kernelized correlation filter algorithm.
Keywords
Visual tracking; Correlation filters; Gaussian distribution; Computer vision; HOG;
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1 B. Babenko, M.-H. Yang, and S. Belongie, "Visual tracking with online multiple instance learning," Computer Vision and Pattern Recognition, CVPR 2009. IEEE Conference on, pp. 983-990, 2009. DOI:10.1109/CVPR.2009.5206737   DOI
2 S. Avidan, "Ensemble tracking," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.29, no.2, pp. 261-271, 2007. DOI:10.1109/TPAMI.2007.35   DOI
3 T. Liu, G. Wang, and Q. Yang, "Real-time part-based visual tracking via adaptive correlation filters," Intelligence, pp. 2345-2390, 2015. DOI:10.1109/CVPR.2015.7299124   DOI
4 A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," Acm computing surveys (CSUR), vol.38, no.4, pp. 13, 2006. DOI:10.1145/1177352.1177355   DOI
5 C. Ma, X. Yang, C. Zhang, and M.-H. Yang, "Long-term correlation tracking," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388-5396, 2015. DOI:10.1109/CVPR.2015.7299177   DOI
6 Henriques, J. F., Caseiro, R., Martins, P., Batista, J. "High-speed tracking with kernelized correlation filters," TPAMI, vol.37, no.3, pp. 583-596, 2015. DOI:10.1109/TPAMI.2014.2345390   DOI
7 Galoogahi, H. K., Sim, T., Lucey, S, "Multichannel correlation filters," ICCV, pp. 4321-4328, 2013. DOI:10.1109/ICCV.2013.381   DOI
8 S. Salti, A. Cavallaro, and L. D. Stefano, "Adaptive appearance modeling for video tracking: Survey and evaluation," Image Processing, IEEE Transactions on, vol.21, no.10, pp. 4334-4348, 2012. DOI:10.1109/TIP.2012.2206035   DOI
9 Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark. In Computer vision and pattern recognition," CVPR, 2013 IEEE Conference on, pp. 2411-2418. 2013.
10 A. Smeulders, D. 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, 2014. DOI:10.1109/TPAMI.2013.230   DOI
11 Boddeti, V.N., Kanade, T., Kumar, B.V.: "Correlation filters for object alignment," CVPR 2013 IEEE Conference on, pp. 2291-2298, 2013. DOI:10.1109/CVPR.2013.297   DOI
12 Galoogahi, H.K., Sim, T., Lucey, S. "Multichannel correlation filters," ICCV, pp. 4321-4328, 2013. DOI:10.1109/ICCV.2013.381   DOI
13 J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High speed tracking with kernelized correlation filters," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.37, no.3, pp. 583-596, 2015. DOI:10.1109/TPAMI.2014.2345390   DOI
14 S. Avidan, "Support vector tracking," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.26, no.8, pp. 1064-1072, 2004. DOI:DOI:10.1109/TPAMI.2004.53   DOI
15 B. Vijaya Kumar, "Minimum-variance synthetic discriminant functions," JOSA A, vol.3, no.10, pp. 1579-1584, DOI:1986.10.1364/JOSAA.3.001579   DOI
16 J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," Computer Vision-ECCV 2012, pp. 702-715, 2012. DOI:10.1007/978-3-642-33765-9_50   DOI
17 Henriques, J. F., Carreira, J., Caseiro, R., Batista, J, "Beyond hard negative mining: Efficient detector learning via block-circulant decomposition," ICCV, pp. 2760-2767, 2013. DOI:10.1109/ICCV.2013.343   DOI
18 Henriques, J. F., Carreira, J., Caseiro, R., Batista, J, "Beyond hard negative mining: Efficient detector learning via block-circulant decomposition," ICCV, pp. 2760-2767, 2013. DOI:10.1109/ICCV.2013.343   DOI
19 Revaud, J., Douze, M., Cordelia, S., Jgou, H, "Event retrieval in large video collections with circulant temporal encoding," CVPR, 2013, pp. 2459-2466.
20 D. S. Bolme, J. R. Beveridge, B. A. Draper, Lui, Y. M, "Visual object tracking using adaptive correlation filters," CVPR, pp. 2544-2550, 2010. DOI:10.1109/CVPR.2010.5539960   DOI
21 M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, L. Cehovin, G. Nebehay, T. Vojir, G. Fernandez, A. Lukezic, A. Dimitriev, et al, "The visual object tracking vot 2014 challenge results," Computer Vision-ECCV 2014 Workshops, 2014, pp. 191-217.
22 D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking. Pattern Analysis and Machine Intelligence," IEEE Transactions on, vol.25, no.5, pp. 564-577, 2003. DOI:10.1109/TPAMI.2003.1195991   DOI
23 T. Zhang, S. L. C. Xu, S. Yan, B. Ghanem, N. Ahuja, and M.-H. Yang, "Structural sparse tracking," Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, pp. 150-158, 2015. DOI:10.1109/CVPR.2015.7298610   DOI
24 T. Poggio and G. Cauwenberghs, "Incremental and decremental support vector machine learning," Advances in neural information processing systems, pp. 409, 2001.
25 A. Adam, E. Rivlin, and I. Shimshoni, "Robust fragmentsbased tracking using the integral histogram," Computer vision and pattern recognition, 2006 IEEE Computer Society Conference on, vol.1, pp. 798-805, 2006. DOI10.1109/CVPR.2006.256   DOI
26 X. Mei and H. Ling, "Robust visual tracking using L1 minimization," Computer Vision, 2009 IEEE 12th International Conference on, pp. 1436-1443, 2009. DOI:10.1109/ICCV.2009.5459292   DOI
27 T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Robust visual tracking via multi-task sparse learning," Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2042-2049, 2012. DOI:10.1109/CVPR.2012.6247908   DOI
28 T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Low-rank sparse learning for robust visual tracking," Computer Vision-ECCV 2012, pp. 470-484, 2012. DOI:10.1007/978-3-642-33783-3_34   DOI