Abstract and Applied Analysis

Vision Target Tracker Based on Incremental Dictionary Learning and Global and Local Classification

Yang Yang, Ming Li, Fuzhong Nian, Huiya Zhao, and Yongfeng He

Full-text: Open access

Abstract

Based on sparse representation, a robust global and local classification algorithm for visual target tracking in uncertain environment was proposed in this paper. The global region of target and the position of target would be found, respectively by the proposed algorithm. Besides, overcompleted dictionary was obtained and updated by biased discriminate analysis with the divergence of positive and negative samples at current frame. And this over-completed dictionary not only discriminates the positive samples accurately but also rejects the negative samples effectively. Experiments on challenging sequences with evaluation of the state-of-the-art methods show that the proposed algorithm has better robustness to illumination changes, perspective changes, and targets rotation itself.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 323072, 10 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393449781

Digital Object Identifier
doi:10.1155/2013/323072

Mathematical Reviews number (MathSciNet)
MR3049417

Zentralblatt MATH identifier
1272.68404

Citation

Yang, Yang; Li, Ming; Nian, Fuzhong; Zhao, Huiya; He, Yongfeng. Vision Target Tracker Based on Incremental Dictionary Learning and Global and Local Classification. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 323072, 10 pages. doi:10.1155/2013/323072. https://projecteuclid.org/euclid.aaa/1393449781


Export citation

References

  • T. Bai and Y. F. Li, “Robust visual tracking with structured sparse representation appearance model,” Pattern Recognition, vol. 45, pp. 2390–2404, 2012.
  • F. Chen, Q. Wang, S. Wang, W. Zhang, and W. Xu, “Object tracking via appearance modeling and sparse representation,” Image and Vision Computing, vol. 29, pp. 787–796, 2011.
  • D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564–577, 2003.
  • 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.
  • B. Babenko, S. Belongie, and M. H. Yang, “Visual tracking with online multiple instance learning,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 983–990, June 2009.
  • Z. Yin and R. T. Collins, “Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, June 2008.
  • S. Avidan, “Ensemble tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 261–271, 2007.
  • X. Mei and H. Ling, “Robust visual tracking using L1 minimization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '09), pp. 1436–1443, 2009.
  • Z. Han, J. Jiao, B. Zhang, Q. Ye, and J. Liu, “Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR),” Pattern Recognition, vol. 44, no. 9, pp. 2170–2183, 2011.
  • B. Liu, L. Yang, J. Huang, P. Meer, L. Gong, and C. Kulikowski, “Robust and fast collaborative tracking with two stage sparse optimization,” Lecture Notes in Computer Science, vol. 6314, no. 4, pp. 624–637, 2010.
  • X. Mei, H. Ling, Y. Wu, E. Blasch, and L. Bai, “Minimum error bounded efficient L1 tracker with occlusion detection,” in Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR '11), pp. 1257–1264, 2011.
  • J. A. Tropp and S. J. Wright, “Computational methods for sparse solution of linear inverse problems,” Proceedings of the IEEE, vol. 98, pp. 948–958, 2010.
  • Q. Zhang and B. Li, “Discriminative K-SVD for dictionary learning in face recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 2691–2698, June 2010.
  • J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online dictionary learning for sparse coding,” in Proceedings of the 26th International Conference On Machine Learning (ICML '09), pp. 689–696, June 2009.
  • M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 543–550, 2011.
  • J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
  • J. Wen, X. Gao, Y. Yuan, D. Tao, and J. Li, “Incremental tensor biased discriminant analysis: a new color-based visual tracking method,” Neurocomputing, vol. 73, no. 4–6, pp. 827–839, 2010.