Visual tracking via discriminative sparse similarity map

IEEE Transactions on Image Processing

Volumn 23, Issue 4, 2014, Pages 1872-1881

  Zhuang, Bohan ^a   Lu, Huchuan ^a   Xiao, Ziyang ^a   Wang, Dong ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

appearance model; Object tracking; sparse representation

Indexed keywords

IMAGE PROCESSING;

APPEARANCE MODELING; COST-PERFORMANCE RATIO; EVALUATION MODELING; EXPERIMENTAL EVALUATION; OBJECT TRACKING; OPTIMIZATION FORMULATIONS; SPARSE REPRESENTATION; STATE-OF-THE-ART METHODS;

TRACKING (POSITION);

ALGORITHM; HUMAN; IMAGE PROCESSING; PROCEDURES; REPRODUCIBILITY; VIDEORECORDING;

ALGORITHMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; VIDEO RECORDING;

EID: 84896963325     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2308414     Document Type: Article

References (26)

1. D. Wang, H. Lu, and M.-H. Yang, "Online object tracking with sparse prototypes, " IEEE Trans. Image Process., vol. 22, no. 1, pp. 314-325, Jan. 2013.
2. C. Bao, Y. Wu, H. Ling, and H. Ji, "Real time robust L1 tracker using accelerated proximal gradient approach, " in Proc. CVPR, 2012, pp. 1830-1837.
3. B. Liu, J. Huang, L. Yang, and C. Kulikowsk, "Robust tracking using local sparse appearance model and k-selection, " in Proc. CVPR, 2011, pp. 1313-1320.
4. X. Jia, H. Lu, and M. Yang, "Visual tracking via adaptive structural local sparse appearance model, " in Proc. CVPR, 2012, pp. 1822-1829.
5. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Robust visual tracking via multi-task sparse learning, " in Proc. CVPR, 2012, pp. 2042-2049.
6. W. Zhong, H. Lu, and M. Yang, "Robust object tracking via sparsity-based collaborative model, " in Proc. CVPR, 2012, pp. 1838-1845.
7. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental learning for robust visual tracking, " Int. J. Comput. Vis., vol. 77, nos. 1-3, pp. 125-141, 2008. (Pubitemid 351294740)
8. Z. Kalal, J. Matas, and K. Mikolajczyk, "P-N learning: Bootstrapping binary classifiers by structural constraints, " in Proc. CVPR, 2010, pp. 49-56.
9. J. Kwon and K. M. Lee, "Visual tracking decomposition, " in Proc. CVPR, 2010, pp. 1269-1276.
10. B. Babenko, M.-H. Yang, and S. Belongie, "Visual tracking with online multiple instance learning, " in Proc. CVPR, 2009, pp. 983-990.
11. A. Adam, E. Rivlin, and I. Shimshoni, "Robust fragments-based tracking using the integral histogram, " in Proc. CVPR, 2006, pp. 798-805. (Pubitemid 44931406)
12. S. Hare, A. Saffari, and P. H. Torr, "Struck: Structured output tracking with kernels, " in Proc. ICCV, 2011, pp. 263-270.
13. M. Godec, P. Roth, and H. Bischof, "Hough-based tracking of non-rigid objects, " in Proc. ICCV, 2011, pp. 81-88.
14. E. G. Learned-Miller and L. S. Lara, "Distribution fields for tracking, " in Proc. CVPR, 2012, pp. 25-33.
15. F. Yang, H. Lu, and M. Yang, "Robust visual tracking via multiple kernel boosting with affinity constraints, " IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 2, pp. 242-254, Jul. 2013.
16. F. Yang, H. Lu, and M.-H. Yang, "Learning structured visual dictionary for object tracking, " Image Vis. Comput., vol. 31, no. 12, pp. 992-999, 2013.
17. P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, "Color-based probabilistic tracking, " in Proc. ECCV, 2002, pp. 661-675.
18. D. Comaniciu, V Ramesh, and P. Meer, "Kernel-based object tracking, " IEEE TPAMI, vol. 25, no. 5, pp. 564-577, May 2003.
19. X. Mei and H. Ling, "Robust visual tracking using l1 minimization, " in Proc. ICCV, 2009, pp. 1-10.
20. X. Mei, H. Ling, Y. Wu, E. Blasch, and L. Bai, "Minimum error bounded efficient l1 tracker with occlusion detection, " in Proc. CVPR, 2011, pp. 1257-1264.
21. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Low-rank sparse learning for robust visual tracking, " in Proc. ECCV, 2012, pp. 470-484.
22. H. Liu and F. Sun, "Visual tracking using sparsity induced similarity, " in Proc. 20th ICPR, 2010, pp. 1702-1705.
23. D. Wang and H. Lu, "On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization, " Signal Process., vol. 93, no. 6, pp. 1608-1623, 2013.
24. D. Wang, H. Lu, and M.-H. Yang, "Least soft-threshold squares tracking, " in Proc. CVPR, 2013, pp. 2371-2378.
25. S. Gao, I. W.-H. Tsang, L.-T. Chia, and P. Zhao, "Local features are not lonely-Laplacian sparse coding for image classification, " in Proc. CVPR, 2010, pp. 3555-3561.
26. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, "The pascal visual object classes (VOC) challenge, " Int. J. Comput. Vis., vol. 88, no. 2, pp. 303-338, 2010.