Sparse coding based visual tracking: Review and experimental comparison

Pattern Recognition

Volumn 46, Issue 7, 2013, Pages 1772-1788

  Zhang, Shengping ^a   Yao, Hongxun ^a   Sun, Xin ^a   Lu, Xiusheng ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Appearance modeling; Dictionary learning; Sparse coding; Sparse representation; Visual tracking

Indexed keywords

APPEARANCE MODELING; DICTIONARY LEARNING; SPARSE CODING; SPARSE REPRESENTATION; VISUAL TRACKING;

PATTERN RECOGNITION; SOFTWARE ENGINEERING;

TRACKING (POSITION);

EID: 84875236224     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.10.006     Document Type: Article

References (69)

1. P. Pérez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: Proceedings of the European Conference on Computer Vision, 2002, pp. 661-675.
2. D. Comaniciu, V. Ramesh, and P. Meer Kernel-based object tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 25 5 2003 564 577
3. H. Zhou, T. Liu, J. Zheng, F. Lin, Y. Pang, and J. Wu Tracking non-rigid objects in video sequences International Journal of Information Acquisition 3 2 2006 131 137
4. D. Ross, J. Lim, R. Lin, and M. Yang Incremental learning for robust visual tracking International Journal of Computer Vision 77 8 2007 125 141
5. H. Zhou, Y. Yuan, and C. Shi Object tracking using sift features and mean shift Computer Vision and Image Understanding 113 3 2009 345 352
6. X. Sun, H. Yao, S. Zhang, A novel supervised level set method for non-rigid object tracking, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2011, pp. 3393-3400.
7. H. Zhou, and H. Hu Human motion tracking for rehabilitation - a survey Biomedical Signal Processing and Control 3 1 2008 1 18
8. H. Yang, L. Shao, F. Zheng, L. Wang, and Z. Song Recent advances and trends in visual tracking a review Neurocomputing 74 18 2011 3823 3831
9. A. Jepson, D. Fleet, and T. EI-Maraghi Robust online appearance models for visual tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 25 10 2003 1296 1311
10. R. Collins, Y. Liu, and M. Leordeanu On-line selection of discriminative tracking features IEEE Transactions on Pattern Analysis and Machine Intelligence 27 10 2005 1631 1643
11. H. Grabner, H. Bischof, On-line boosting and vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 260-267.
12. H. Grabner, C. Leistner, Semi-supervised on-line boosting for robust tracking, in: Proceedings of the 10th European Conference on Computer Vision, 2008, pp. 234-247.
13. B. Babenko, M. Yang, and S. Belongie Robust object tracking with online multiple instance learning IEEE Transactions on Pattern Analysis and Machine Intelligence 33 8 2011 1619 1632
14. C. Kuo, C. Huang, R. Nevatia, Multi-target tracking by on-line learned discriminative appearance models, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 1-8.
15. D. Donoho Compressed sensing IEEE Transactions on Information Theory 52 4 2006 1289 1306
16. E. Candès, J. Romberg, and T. Tao Robust uncertainty principles exact signal reconstruction from highly incomplete frequency information IEEE Transactions on Information Theory 52 2 2006 489 509
17. B. Olshausen, and D. Field Emergence of simple-cell receptive field properties by learning a sparse code for natural images Nature 381 1996 607 609
18. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma Robust face recognition via sparse representation IEEE Transactions on Pattern Analysis and Machine Intelligence 31 2 2009 210 227
19. X. Mei, H. Ling, Robust visual tracking using L1 minimization, Proceedings of the 12th International Conference on Computer Vision, 2009, pp. 1436-1443.
20. M. Riesenhuber, and T. Poggio Hierarchical models of object recognition in cortex Nature Neuroscience 2 11 1999 1019 1025
21. S. Zhang, H. Yao, S. Liu, Robust visual tracking using feature-based visual attention, in: Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing, 2010, pp. 1150-1153.
22. D. Field What is the goal of sensory coding ? Neural Computation 6 4 1994 559 601
23. D. Donoho For most large underdetermined systems of linear equations the minimal L1-norm solution is also the sparsest solution Communications on Pure and Applied Mathematics 59 6 2006 797 829
24. M. Figueiredo, R. Nowak, and S. Wright Gradient projection for sparse reconstruction application to compressed sensing and other inverse problems IEEE Journal of Selected Topics in Signal Processing 1 4 2007 586 597
25. 1-regularized least squares IEEE Journal of Selected Topics in Signal Processing 1 4 2007 606 617
26. K. Engan, S. Aase, and J. Husoy Frame based signal compression using method of optimal directions Proceedings of the IEEE International Symposium on Circuits and Systems 4 1999 1 4
27. M. Aharon, M. Elad, and A. Bruckstein Svd an algorithm for designing overcomplete dictionaries for sparse representation IEEE Transactions on Signal Processing 54 11 2006 4311 4322
28. K. Fukushima Neocognitron a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position Biological Cybernetics 36 4 1980 193 202
29. D. Perrett, and M. Oram Neurophsyiology of shape processing Image and Vision Computing 11 6 1993 317 333
30. T. Serre, L. Wolf, T. Poggio, Object recognition with features inspired by visual cortex, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 994-1000.
31. J. Mutch, and D. Lowe Object class recognition and localization using sparse features with limited receptive fields International Journal of Computer Vision 80 1 2008 45 57
32. D. Gabor Theory of communication Journal of the Institution of Electrical Engineers 94 73 1947 429 459
33. B. Olshausen, and D. Field Sparse coding with an overcomplete basis set a strategy employed by V1? Vision Research 37 1997 3311 3325
34. J. Yang, K. Yu, Y. Gong, T. Huang, Linear spatial pyramid matching using sparse coding for image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1794-1801
35. L. Fei-Fei, P. Perona, A Bayesian hierarchical model for learning natural scene categories, in: Proceedings of International Conference on Computer Vision and Pattern Recognition, 2005, pp. 524-531.
36. D. Lowe Distinctive image features from scale-invariant keypoints International Journal of Computer Vision 60 2 2004 91 110
37. A. Bell, and T. Sejnowski The independent components of natural scenes are edge filters Vision Research 37 23 1997 3327 3338
38. M. Swain, and D. Ballard Color indexing International Journal of Computer Vision 7 1 1991 11 32
39. Q. Wang, F. Chen, J. Yang, W. Xu, and M. Yang Transferring visual prior for online object tracking IEEE Transactions on Image Processing 21 7 2012 3296 3305
40. B. Liu, J. Huang, L. Yang, C. Kulikowsk, Robust tracking using local sparse appearance model and K-selection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1313-1320.
41. Q. Wang, F. Chen, W. Xu, M. Yang, Online discriminative object tracking with local sparse representation, in: Proceedings of the IEEE Workshop on the Applications of Computer Vision, 2012, pp. 425-432
42. X. Jia, H. Lu, M. Yang, Visual tracking via adaptive structural local sparse appearance model, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1822-1829.
43. S. Zhang, H. Yao, X. Sun, S. Liu, Robust object tracking based on sparse representation, in: Proceedings of the International Conference on Visual Communications and Image Processing, pp. 77441N-1-8, 2010.
44. G. Tzimiropoulos, S. Zafeiriou, M. Pantic, Sparse representations of image gradient orientations for visual recognition and tracking, in: Proceedings of the CVPR Workshop for Human Behaviour Analysis, 2011, pp. 26-33.
45. Y. Wu, E. Blasch, G. Chen, L. Bai, H. Ling, Multiple source data fusion via sparse representation for robust visual tracking, in: Proceedings of the 13th Conference on Information Fusion, 2011, pp. 1-8.
46. X. Mei, H. Ling, Y. Wu, E. Blasch, L. Bai, Minimum error bounded efficient l1 tracker with occlusion detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1257-1264.
47. C. Bao, Y. Wu, H. Ling, H. Ji, Real time robust l1 tracker using accelerated proximal gradient approach, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1830-1837.
48. M. Yang, L. Zhang, Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary, in: Proceedings of the 11th European Conference on Computer Vision, 2010, pp. 448-461.
49. S. Zhang, H. Yao, H. Zhou, X. Sun, and S. Liu Robust visual tracking based on online learning sparse representation Neurocomputing 100 2013 31 40
50. J. Mairal, F. Bach, J. Ponce, G. Sapiro, Online dictionary learning for sparse coding, in: Proceedings of the 26th Annual International Conference on Machine Learning, vol. 382, 2009, pp. 689-696.
51. H. Ling, L. Bai, E. Blasch, X. Mei, Robust infrared vehicle tracking across target pose change using L1 regularization, in: Proceedings of the 13th Conference on Information Fusion, 2010, pp. 1-8.
52. B. Liu, L. Yang, J. Huang, P. Meer, L. Gong, C. Kulikowski, Robust and fast collaborative tracking with two stage sparse optimization, in: Proceedings of the 11th European Conference on Computer Vision, 2010, pp. 624-637.
53. Y. Wu, H. Ling, E. Blasch, L. Bai, G. Chen, Visual tracking based on log-Euclidean Riemannian sparse representation, in: Proceedings of the Seventh International Symposium on Advances in Visual Computing, 2011, pp. 738-747.
54. H. Li, C. Shen, Q. Shi, Real-time visual tracking using compressive sensing, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1305-1312.
55. K. Zhang, L. Zhang, M. Yang, Real-time compressive tracking, European Conference on Computer Vision, in: Proceedings of the 12th European Conference on Computer Vision, 2012, pp. 864-877.
56. M. Yang, L. Zhang, J. Yang, D. Zhang, Robust sparse coding for face recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 625-632
57. J. Yan, M. Tong, Weighted sparse coding residual minimization for visual tracking, in: Proceedings of the Second International Conference on Internet Multimedia Computing and Service, 2011, pp. 1-4.
58. H. Liu, F. Sun, M. Gao, Visual tracking using iterative sparse approximation, in: Proceedings of the Eighth International Symposium on Neural Networks, 2011, pp. 207-214.
59. W. Zhong, H. Lu, M. Yang, Robust object tracking via sparsity-based collaborative model, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1838-1845.
60. A. Adam, E. Rivlin, I. Shimshoni, Robust fragments-based tracking using the integral histogram, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 798-805.
61. M. Everingham, L. Gool, C. Williams, J. Winn, and A. Zisserman The Pascal visual object classes challenge International Journal of Computer Vision 88 2 2010 303 338
62. R. Rigamonti, M. Brown, V. Lepetit, Are sparse representations really relevant for image classification, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2011, pp. 1545-1552.
63. Q. Shi, A. Eriksson, A. Hengel, C. Shen, Is face recognition really a compressive sensing problem, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2011, pp. 553-560.
64. X. Li, C. Shen, Q. Shi, A. Dick, A. Hengel, Non-sparse linear representations for visual tracking with online reservoir metric learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1760-1767.
65. Q. Zhang, B. Li, Discriminative K-SVD for dictionary learning in face recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 1-8.
66. Z. Jiang, Z. Lin, L. Davis, Learning a discriminative dictionary for sparse coding via label consistent k-svd, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2011, pp. 1697-1704.
67. J. Huang, T. Zhang, D. Metaxas, Learning with structured sparsity, Journal of Machine Learning Research 12 (2011) 3371-3412.
68. K. Yu, Y. Lin, J. Lafferty, Learning image representations from the pixel level via hierarchical sparse coding, in: Proceedings of the International Conference on Computer Vision and Pattern Recognition, 2011, pp. 1713-1720.
69. X. Yuan, S. Yan, Visual classification with multi-task joint sparse representation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 3493-3500.