Mathematical Problems in Engineering
Volume 2015, Article ID 238971, 13 pages
http://dx.doi.org/10.1155/2015/238971
Research Article
Robust Visual Correlation Tracking
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3North Automatic Control Technology Research Institute, Taiyuan 030006, China
Received 3 June 2015; Accepted 2 September 2015
Academic Editor: Matteo Gaeta
Copyright © 2015 Lei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, “Adaptive color attributes for real-time visual tracking,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1090–1097, IEEE, Columbus, Ohio, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
- J. Fang, Q. Wang, and Y. Yuan, “Part-based online tracking with geometry constraint and attention selection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 5, pp. 854–864, 2014. View at Publisher · View at Google Scholar · View at Scopus
- X. Q. Zhang, W. M. Hu, S. Y. Chen, and S. Maybank, “Graph-embedding-based learning for robust object tracking,” IEEE Transactions on Industrial Electronics, vol. 61, no. 2, pp. 1072–1084, 2014. View at Publisher · View at Google Scholar · View at Scopus
- E. Chen, O. Haik, and Y. Yitzhaky, “Detecting and tracking moving objects in long-distance imaging through turbulent medium,” Applied Optics, no. 6, pp. 1181–1190, 2014. View at Publisher · View at Google Scholar · View at Scopus
- L. Sevilla-Lara and E. Learned-Miller, “Distribution fields for tracking,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1910–1917, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
- T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, “Robust visual tracking via multi-task sparse learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 2042–2049, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar
- 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. View at Publisher · View at Google Scholar · View at Scopus
- J. M. Danell, G. Hager, F. S. Khan, and M. Felsberg, “Accurate scale estimation for robust visual tracking,” in Proceedings of the British Machine Vision Conference (BMVC '14), Nottingham, UK, September 2014.
- H. Grabner, M. Grabner, and H. Bischof, “Real-time tracking via on-line boosting,” in Proceedings of the 17th British Machine Vision Conference, pp. 47–56, September 2006. View at Scopus
- 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. View at Publisher · View at Google Scholar · View at Scopus
- S. Wang, H.-C. Lu, F. Yang, and M.-H. Yang, “Superpixel tracking,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 1323–1330, IEEE, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
- K. H. Zhang, L. Zhang, and M. H. Yang, “Fast compressive tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 10, pp. 2002–2015, 2014. View at Publisher · View at Google Scholar
- F. Yang, H. C. Lu, and M.-H. Yang, “Robust superpixel tracking,” IEEE Transactions on Image Processing, vol. 23, no. 4, pp. 1639–1651, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
- S. Avidan, “Ensemble tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 261–271, 2007. View at Publisher · View at Google Scholar · View at Scopus
- Z. Kalal, J. Matas, and K. Mikolajczyk, “P-N learning: bootstrapping binary classifiers by structural constraints,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 49–56, San Diego, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
- S. Hare, A. Saffari, and P. H. S. Torr, “Struck: structured output tracking with kernels,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 263–270, IEEE, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
- K. H. Zhang, L. Zhang, and M.-H. Yang, “Real-time compressive tracking,” in Proceedings of the European Conference on Computer Vision, pp. 864–877, 2012.
- L. Sevilla-Lara and E. Learned-Miller, “Distribution fields for tracking,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1910–1917, Providence, RI, USA, June 2012.
- Q. Tu, Y. P. Xu, and M. L. Zhou, “Robust vehicle tracking based on Scale Invariant Feature Transform,” in Proceedings of the IEEE International Conference on Information and Automation (ICIA '08), pp. 86–90, Changsha, China, June 2008. View at Publisher · View at Google Scholar · View at Scopus
- M. Jiang, L. Zhang, and Y. L. Huang, “Object tracking based on particle filter and scale invariant feature transform,” in Proceedings of the IEEE International Conference on Multimedia Technology (ICMT '10), pp. 1–4, IEEE, Ningbo, China, October 2010. View at Publisher · View at Google Scholar · View at Scopus
- L. Wei, X. Xudong, W. Jianhua, Z. Yi, and H. Jianming, “A SIFT-based mean shift algorithm for moving vehicle tracking,” in Proceedings of the 25th IEEE Intelligent Vehicles Symposium (IV '14), pp. 762–767, Dearborn, Mich, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
- D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui, “Visual object tracking using adaptive correlation filters,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 2544–2550, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
- B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlation Pattern Recognition, Cambridge University Press, 2005. View at Publisher · View at Google Scholar
- J. Henriques, R. Caseiro, P. Martins, and J. Batista, “Exploiting the circulant structure of tracking-by-detection with kernels,” in Proceedings of the European Conference on Computer Vision, pp. 702–715, Florence, Italy, October 2012.
- J. F. Henriques, J. Carreira, R. Caseiro, and J. Batista, “Beyond hard negative mining: efficient detector learning via block-circulant decomposition,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 2760–2767, Sydney, Australia, December 2013. View at Publisher · View at Google Scholar · View at Scopus
- H. K. Galoogahi, T. Sim, and S. Lucey, “Multi-channel correlation filters,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '13), pp. 3072–3079, IEEE, Sydney, Australia, December 2013. View at Publisher · View at Google Scholar · View at Scopus
- V. N. Boddeti, T. Kanade, and B. V. K. V. Kumar, “Correlation filters for object alignment,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 2291–2298, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
- 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, 2015. View at Publisher · View at Google Scholar · View at Scopus
- Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: a benchmark,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 2411–2418, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
- R. M. Gray, Toeplitz and Circulant Matrices: A Review, Now Publishers, Norwell, Mass, USA, 2006.
- R. Rifkin, G. Yeo, and T. Poggio, “Regularized least-squares classification,” in Nato Science Series Sub Series III Computer and Systems Sciences, vol. 190, pp. 131–154, IOS Press, Amsterdam, The Netherlands, 2003. View at Google Scholar
- B. Schőlkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2002.
- R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2008.
- K. H. Zhang and H. H. Song, “Real-time visual tracking via online weighted multiple instance learning,” Pattern Recognition, vol. 46, no. 1, pp. 397–411, 2013. View at Publisher · View at Google Scholar · View at Scopus