Robust Visual Tracking via Multiple Kernel Boosting with Affinity Constraints

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 24, Issue 2, 2014, Pages 242-254

  Yang, Fan ^a,b   Lu, Huchuan ^a   Yang, Ming Hsuan ^c  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b University of Maryland ^*  (United States)

^c Merced   (United States)

Author keywords

Affinity constraint; multiple kernel learning; object tracking

Indexed keywords

ALGORITHMS; SUPPORT VECTOR MACHINES; TRACKING (POSITION);

AFFINITY CONSTRAINT; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OBJECT TRACKING; OPTIMAL COMBINATION; PROBABILISTIC FRAMEWORK; SPATIAL INFORMATIONS; STATE-OF-THE-ART ALGORITHMS;

LEARNING ALGORITHMS;

EID: 84978069682     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2013.2276145     Document Type: Article

References (54)

1. B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in Proc. IJCAI, 1981, pp. 674-679.
2. M. J. Black and A. D. Jepson, "Eigentracking: Robust matching and tracking of articulated objects using a view-based representation," in Proc. ECCV, 1996, pp. 329-342.
3. D. Comaniciu, V. Ramesh, P. Meer, "Real-time tracking of non-rigid objects using mean shift," in Proc. CVPR, 2000, pp. 2142-2150.
4. D. A. Ross, J. Lim, M.-H. Yang, "Adaptive probabilistic visual tracking with incremental subspace update," in Proc. ECCV, 2004, pp. 470-482.
5. H. Wang, D. Suter, K. Schindler, C. Shen, "Adaptive object tracking based on an effective appearance filter," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 9, pp. 1661-1667, Sep. 2007.
6. D. A. Ross, J. Lim, R.-S. Lin, M.-H. Yang, "Incremental learning for robust visual tracking," Int. J. Comput. Vision, vol. 77, nos. 1-3, pp. 125-141, 2008.
7. D. Wang, H. Lu, M.-H. Yang, "Least soft-threshold squares tracking," in Proc. CVPR, 2013, pp. 2371-2378.
8. M. Isard and A. Blake, "Contour tracking by stochastic propagation of conditional density," in Proc. ECCV, 1996, pp. 343-356.
9. P. Pérez, C. Hue, J. Vermaak, M. Gangnet, "Color-based probabilistic tracking," in Proc. ECCV, 2002, pp. 661-675.
10. S. Avidan, "Support vector tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 1064-1072, Aug. 2004.
11. S. Avidan, "Ensemble tracking," in Proc. CVPR, 2005, pp. 494-501.
12. R. T. Collins, Y. Liu, M. Leordeanu, "Online selection of discriminative tracking features," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1631-1643, Oct. 2005.
13. H. Grabner and H. Bischof, "On-line boosting and vision," in Proc. CVPR, 2006, pp. 260-267.
14. H. Wang and D. Suter, "A consensus-based method for tracking: Modelling background scenario and foreground appearance," Pattern Recognit., vol. 40, no. 3, pp. 1091-1105, 2007.
15. B. Babenko, M.-H. Yang, S. Belongie, "Visual tracking with online multiple instance learning," in Proc. CVPR, 2009, pp. 983-990.
16. X. Li, A. Dick, H. Wang, C. Shen, A. van den Hengel, "Graph modebased contextual kernels for robust SVM tracking," in Proc. ICCV, 2011, pp. 1156-1163.
17. S. Wang, H. Lu, F. Yang, M.-H. Yang, "Superpixel tracking," in Proc. ICCV, 2011, pp. 1323-1330.
18. C. Shen, J. Kim, H. Wang, "Generalized kernel-based visual tracking," IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 1, pp. 119-130, Jan. 2010.
19. J. Kwon and K. M. Lee, "Visual tracking decomposition," in Proc. CVPR, 2010, pp. 1269-1276.
20. X. Li, A. Dick, C. Shen, A. van den Hengel, H. Wang, "Incremental learning of 3D-DCT compact representations for robust visual tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 4, pp. 863-881, Apr. 2013.
21. X. Mei and H. Ling, "Robust visual tracking using L1 minimization," in Proc. ICCV, 2009, pp. 1436-1443.
22. D. Wang, H. Lu, M.-H. Yang, "Online object tracking with sparse prototypes," IEEE Trans. Image Process., vol. 22, no. 1, pp. 314-325, Jan. 2013.
23. B. Liu, J. Huang, L. Yang, C. A. Kulikowski, "Robust tracking using local sparse appearance model and k-selection," in Proc. CVPR, 2011, pp. 1313-1320.
24. W. Zhong, H. Lu, M.-H. Yang, "Robust object tracking via sparsitybased collaborative model," in Proc. CVPR, 2012, pp. 1838-1845.
25. X. Jia, H. Lu, M.-H. Yang, "Visual tracking via adaptive structural local sparse appearance model," in Proc. CVPR, 2012, pp. 1822-1829.
26. T. Zhang, B. Ghanem, S. Liu, N. Ahuja, "Robust visual tracking via multi-task sparse learning," in Proc. CVPR, 2012, pp. 2042-2049.
27. P. Gehler and S. Nowozin, "On feature combination for multiclass object classification," in Proc. ICCV, 2009, pp. 221-228.
28. B. Siddiquie, S. Vitaladevuni, L. Davis, "Combining multiple kernels for efficient image classification," in Proc. WACV, 2009, pp. 1-8.
29. F. Yang, H. Lu, Y.-W. Chen, "Human tracking by multiple kernel boosting with locality affinity constraints," in Proc. ACCV, 2010, pp. 39-50.
30. F. Bach, G. Lanckriet, M. Jordan, "Multiple kernel learning, conic duality, the SMO algorithm," in Proc. ICML, 2004, pp. 6-13.
31. A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet, "Simplemkl," J. Mach. Learning Res., vol. 9, no. 1, pp. 2491-2521, 2008.
32. M. Gönen and E. Alpaydin, "Localized multiple kernel learning," in Proc. ICML, 2008, pp. 352-359.
33. M. Christoudias, R. Urtasun, T. Darrell, "Bayesian localized multiple kernel learning," EECS Dept., Univ. California, Berkeley, CA, USA, Tech. Rep. UCB/EECS-2009-96, Jul. 2009.
34. L. Cao, J. Luo, F. Liang, T. Huang, "Heterogeneous feature machines for visual recognition," in Proc. ICCV, 2009, pp. 1095-1102.
35. J. Yang, Y. Li, Y. Tian, L. Duan, W. Gao, "Group-sensitive multiple kernel learning for object categorization," in Proc. ICCV, 2009, pp. 436-443.
36. A. Blum and T. M. Mitchell, "Combining labeled and unlabeled SATA with co-training," in Proc. COLT, 1998, pp. 92-100.
37. S. P. Abney, "Bootstrapping," in Proc. ACL, 2002, pp. 360-367.
38. V. Sindhwani, P. Niyogi, M. Belkin, "Beyond the point cloud: From transductive to semi-supervised learning," in Proc. ICML, 2005, pp. 824-831.
39. I. Muslea, S. Minton, C. A. Knoblock, "Active learning with multiple views," J. Artif. Intell. Res., vol. 27, no. 1, pp. 203-233, 2006.
40. B. Long, P. S. Yu, Z. M. Zhang, "A general model for multiple view unsupervised learning," in Proc. SDM, 2008, pp. 822-833.
41. F. Tang, S. Brennan, Q. Zhao, H. Tao, "Co-tracking using semisupervised support vector machines," in Proc. ICCV, 2007, pp. 1-8.
42. C. Christoudias, R. Urtasun, T. Darrell, "Multi-view learning in the presence of view disagreement," in Proc. UAI, 2008, pp. 88-96.
43. Z. Yin, F. Porikli, R. T. Collins, "Likelihood map fusion for visual object tracking." in Proc. WACV, 2008, pp. 1-7.
44. M. Yang, F. Lv, W. Xu, Y. Gong, "Detection driven adaptive multicue integration for multiple human tracking," in Proc. ICCV, 2009, pp. 1554-1561.
45. H. Lu, S. Lu, D. Wang, S. Wang, H. Leung, "Pixel-wise spatial pyramid-based hybrid tracking," IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 9, pp. 1365-1376, Sep. 2012.
46. D. Wang, H. Lu, Y.-W. Chen, "Object tracking by multi-cues spatial pyramid matching." in Proc. ICIP, 2010, pp. 3957-3960.
47. J. H. Yoon, D. Y. Kim, K.-J. Yoon, "Visual tracking via adaptive tracker selection with multiple features," in Proc. ECCV, 2012, pp. 28-41.
48. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. CVPR, 2005, pp. 886-893.
49. D. Lowe, "Object recognition from local scale-invariant features," in Proc. ICCV, 1999, pp. 1150-1157.
50. M. Gönen and E. Alpaydin, "Localized algorithms for multiple kernel learning," Pattern Recognit., vol. 46, no. 3, pp. 795-807, 2013.
51. J. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
52. S. Keerthi and C.-J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural Comput., vol. 15, no. 7, pp. 1667-1689, 2003.
53. A. Adam, E. Rivlin, I. Shimshoni, "Robust fragments-based tracking using the integral histogram," in Proc. CVPR, 2006, pp. 798-805.
54. A. Vedaldi and B. Fulkerson. (2008). VLFeat: An Open and Portable Library of Computer Vision Algorithms [Online]. Available: http://www.vlfeat.org/