Advertisement

International Journal of Computer Vision

, Volume 126, Issue 5, pp 515–536 | Cite as

Visual Tracking via Subspace Learning: A Discriminative Approach

  • Yao SuiEmail author
  • Yafei Tang
  • Li Zhang
  • Guanghui Wang
Article

Abstract

Good tracking performance is in general attributed to accurate representation over previously obtained targets and/or reliable discrimination between the target and the surrounding background. In this work, a robust tracker is proposed by integrating the advantages of both approaches. A subspace is constructed to represent the target and the neighboring background, and their class labels are propagated simultaneously via the learned subspace. In addition, a novel criterion is proposed, by taking account of both the reliability of discrimination and the accuracy of representation, to identify the target from numerous target candidates in each frame. Thus, the ambiguity in the class labels of neighboring background samples, which influences the reliability of the discriminative tracking model, is effectively alleviated, while the training set still remains small. Extensive experiments demonstrate that the proposed approach outperforms most state-of-the-art trackers.

Keywords

Visual tracking Discriminative subspace Joint learning Sparse representation Low-rank approximation 

References

  1. Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing (TSP), 50(2), 174–188.CrossRefGoogle Scholar
  2. Avidan, S. (2004). Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 26(8), 1064–1072.CrossRefGoogle Scholar
  3. Avidan, S. (2007). Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 29(2), 261–271.CrossRefGoogle Scholar
  4. Babenko, B., Member, S., Yang, M. H., & Member, S. (2011). Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(8), 1619–1632.CrossRefGoogle Scholar
  5. Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1), 183–202.MathSciNetCrossRefzbMATHGoogle Scholar
  6. Cai, J., Candès, E., & Shen, Z. (2010). A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4), 1956–1982.MathSciNetCrossRefzbMATHGoogle Scholar
  7. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis? Journal of the ACM, 58(3), 1–37.MathSciNetCrossRefzbMATHGoogle Scholar
  8. Danelljan, M., Häger, G., Khan, F. S., & Felsberg, M. (2014). Accurate scale estimation for robust visual tracking. In British machine vision conference (BMVC) Google Scholar
  9. Dinh, T. B., Vo, N., & Medioni, G. (2011). Context tracker: Exploring supporters and distracters in unconstrained environments. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1177–1184).Google Scholar
  10. Grabner, H., & Bischof, H. (2006). On-line boosting and vision. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR), (Vol. 1, pp. 260–267)Google Scholar
  11. Hager, G. D., & Belhumeur, P. N. (1996). Real-time tracking of image regions with changes in geometry and illumination. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 403–410).Google Scholar
  12. Hare, S., Saffari, A., & Torr, P. (2011). Struck: Structured output tracking with kernels. In IEEE international conference on computer vision (ICCV) (pp. 263–270).Google Scholar
  13. Henriques, F., Caseiro, R., Martins, P., & Batista, J. (2012). Exploiting the circulant structure of tracking-by-detection with kernels. In European conference on computer vision (ECCV) (pp 702–715)Google Scholar
  14. Henriques, J., Caseiro, R., Martins, P., & Batista, J. (2015). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 37(3), 583–596.CrossRefGoogle Scholar
  15. Isard, M. (1998). CONDENSATION: Conditional density propagation for visual tracking. International Journal of Computer Vision (IJCV), 29(1), 5–28.CrossRefGoogle Scholar
  16. Jia, X., Lu, H., & Yang, M. H. (2012). Visual tracking via adaptive structural local sparse appearance model. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1822–1829).Google Scholar
  17. Kalal, Z., Matas, J., & Mikolajczyk, K. (2010). P-N learning: Bootstrapping binary classifiers by structural constraints. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 49–56).Google Scholar
  18. Kalal, Z., Mikolajczyk, K., & Matas, J. (2012). Tracking–learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(7), 1409–1422.CrossRefGoogle Scholar
  19. Kriegmant, D. J., Engineering, E., & Haven, N. (1996). What is the set of images of an object under all possible lighting conditions? In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 270–277).Google Scholar
  20. Kwon, J., & Lee, K. (2010). Visual tracking decomposition. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1269–1276).Google Scholar
  21. Kwon, J., & Lee, K. M. (2011). Tracking by sampling trackers. In IEEE international conference on computer vision (ICCV) (pp. 1195–1202).Google Scholar
  22. Kwon, J., & Lee, K. M. (2014). Tracking by sampling and integrating multiple trackers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 36(7), 1428–1441.MathSciNetCrossRefGoogle Scholar
  23. Lasserre, J. A., Bishop, C. M., & Minka, T. P. (2006). Principled hybrids of generative and discriminative models. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (Vol. 6, pp. 87–94).Google Scholar
  24. Lin, Z., Chen, M., & Ma, Y. (2010). The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC Technical Report (pp. 1–23).Google Scholar
  25. Liu, B., Huang, J., Yang, L., & Kulikowsk, C. (2011). Robust tracking using local sparse appearance model and K-selection. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1313–1320).Google Scholar
  26. Liu, S., Zhang, T., Cao, X., & Xu, C. (2016). Structural correlation filter for robust visual tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR).Google Scholar
  27. Liu, B., Huang, J., Kulikowski, C., & Yang, L. (2013). Robust visual tracking using local sparse appearance model and K-selection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 35(12), 2968–2981.CrossRefGoogle Scholar
  28. Ma, C., Huang, J. B., Yang, X., & Yang, M. H. (2015a). Hierarchical convolutional features for visual tracking. In IEEE international conference on computer vision (ICCV) (pp. 3074–3082).Google Scholar
  29. Ma, C., Yang, X., Zhang, C., & Yang, Mh. (2015b). Long-term correlation tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 5388–5396).Google Scholar
  30. Mairal, J., Bach, F., & Ponce, J. (2008). Discriminative learned dictionaries for local image analysis. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR).Google Scholar
  31. Mei, X., & Ling, H. (2009). Robust visual tracking using L1 minimization. In IEEE international conference on computer vision (ICCV) (pp. 1436–1443).Google Scholar
  32. Mei, X., & Ling, H. (2011). Robust visual tracking and vehicle classification via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(11), 2259–2272.CrossRefGoogle Scholar
  33. Nam, H., & Han, B. (2016). Learning multi-domain convolutional neural networks for visual tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR).Google Scholar
  34. Ng, A. Y., & Jordan, M. I. (2001). On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In Advances in Neural Information Processing Systems (NIPS) (pp. 841–848).Google Scholar
  35. Pati, Y., Rezaiifar, R., & Krishnaprasad, P. (1993). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Asilomar conference on signals, systems and computers (pp. 40–44).Google Scholar
  36. Pham, D. S., & Venkatesh, S. (2008). Joint learning and dictionary construction for pattern recognition. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1–8).Google Scholar
  37. Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., & Yang, M. H. (2016). Hedged deep tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 4303–4311).Google Scholar
  38. Raina, R., & Ng, A. Y. (2007). Self-taught learning : Transfer learning from unlabeled data. In International conference on machine learning (ICML).Google Scholar
  39. Ross, D. A., Lim, J., Lin, R. S., & Yang, M. H. (2007). Incremental learning for robust visual tracking. International Journal of Computer Vision (IJCV), 77(1–3), 125–141.Google Scholar
  40. Sevilla-Lara, L., & Learned-Miller, E. (2012). Distribution fields for tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1910–1917).Google Scholar
  41. Smeulders, A. W. M., Chu, D. M., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. (2014). Visual tracking: An experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 36(7), 1442–1468.CrossRefGoogle Scholar
  42. Sui, Y., Tang, Y., & Zhang, L. (2015a). Discriminative low-rank tracking. In IEEE international conference on computer vision (ICCV) (pp. 3002–3010).Google Scholar
  43. Sui, Y., Wang, G., & Zhang, L. (2017). Correlation filter learning toward peak strength for visual tracking. IEEE Transactions on Cybernetics (TCyb).  https://doi.org/10.1109/TCYB.2017.2690860.
  44. Sui, Y., Wang, G., Tang, Y., & Zhang, L. (2016a). Tracking completion. In European conference on computer vision (ECCV).Google Scholar
  45. Sui, Y., Zhang, Z., Wang, G., Tang, Y., & Zhang, L. (2016b). Real-time visual tracking: Promoting the robustness of correlation filter learning. In European conference on computer vision (ECCV) Google Scholar
  46. Sui, Y., & Zhang, L. (2015). Visual tracking via locally structured Gaussian process regression. IEEE Signal Processing Letters, 22(9), 1331–1335.CrossRefGoogle Scholar
  47. Sui, Y., & Zhang, L. (2016). Robust tracking via locally structured representation. International Journal of Computer Vision (IJCV), 119(2), 110–144.MathSciNetCrossRefGoogle Scholar
  48. Sui, Y., Zhang, S., & Zhang, L. (2015b). Robust visual tracking via sparsity-induced subspace learning. IEEE Transactions on Image Processing (TIP), 24(12), 4686–4700.MathSciNetCrossRefGoogle Scholar
  49. Sui, Y., Zhao, X., Zhang, S., Yu, X., Zhao, S., & Zhang, L. (2015c). Self-expressive tracking. Pattern Recognition (PR), 48(9), 2872–2884.CrossRefGoogle Scholar
  50. Tang, M., & Feng, J. (2015). Multi-kernel correlation filter for visual tracking. In IEEE international conference on computer vision (ICCV) (pp. 3038–3046).Google Scholar
  51. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological), 58(1), 267–288.MathSciNetzbMATHGoogle Scholar
  52. Wang, D., & Lu, H. (2012). Object tracking via 2DPCA and L1-regularization. IEEE Signal Processing Letters, 19(11), 711–714.CrossRefGoogle Scholar
  53. Wang, D., & Lu, H. (2014). Visual tracking via probability continuous outlier model. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR).Google Scholar
  54. Wang, D., Lu, H., & Yang, M. H. (2013a). Least soft-thresold squares tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 2371–2378).Google Scholar
  55. Wang, D., Lu, H., & Yang, M. H. (2013b). Online object tracking with sparse prototypes. IEEE Transactions on Image Processing (TIP), 22(1), 314–325.MathSciNetCrossRefzbMATHGoogle Scholar
  56. Wang, L., Ouyang, W., Wang, X., & Lu, H. (2015). Visual tracking with fully convolutional networks. In IEEE international conference on computer vision (ICCV) (pp. 3119–3127).Google Scholar
  57. Wang, L., Ouyang, W., Wang, X., & Lu, H. (2016). Stct: Sequentially training convolutional networks for visual tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1373–1381).Google Scholar
  58. Wang, Q., Chen, F., Xu, W., & Yang, M. (2012). Online discriminative object tracking with local sparse representation. In IEEE winter conference on applications of computer vision (WACV).Google Scholar
  59. Wright, J., Ma, Y., Mairal, J., & Sapiro, G. (2010). Sparse representation for computer vision and pattern recognition. Proceedings of The IEEE, 98(6), 1031–1044.CrossRefGoogle Scholar
  60. Wu, Y., Lim, J., & Yang, M. H. (2013). Online object tracking: A benchmark. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 2411–2418).Google Scholar
  61. Wu, Y., Lim, J., & Yang, M. H. (2015). Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 37(9), 1834–1848.CrossRefGoogle Scholar
  62. Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: A survey. ACM Computing Surveys, 38(4), 13–57.CrossRefGoogle Scholar
  63. Zhang, C., Liu, R., Qiu, T., & Su, Z. (2014a). Robust visual tracking via incremental low-rank features learning. Neurocomputing, 131, 237–247.CrossRefGoogle Scholar
  64. Zhang, K., Liu, Q., Wu, Y., & Yang, M. H. (2016a). Robust visual tracking via convolutional networks without training. IEEE Transactions on Image Processing (TIP), 25(4), 1779–1792.MathSciNetGoogle Scholar
  65. Zhang, K., Zhang, L., & Yang, M. H. (2012a). Real-time compressive tracking. In European conference on computer vision (ECCV) (pp. 866–879).Google Scholar
  66. Zhang, K., Zhang, L., & Yang, M. H. (2013a). Real-time object tracking via online discriminative feature selection. IEEE Transactions on Image Processing (TIP), 22(12), 4664–4677.MathSciNetCrossRefzbMATHGoogle Scholar
  67. Zhang, T., Bibi, A., & Ghanem, B. (2016b). In defense of sparse tracking: Circulant sparse tracker. In CVPR.Google Scholar
  68. Zhang, T., Ghanem, B., Liu, S., & Ahuja, N. (2012b). Low-rank sparse learning for robust visual tracking. In European conference on computer vision (ECCV) (pp. 470–484).Google Scholar
  69. Zhang, T., Liu, S., Xu, C., Yan, S., Ghanem, B., Ahuja, N., & Yang, Mh. (2015). Structural sparse tracking. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 150–158).Google Scholar
  70. Zhang, T., Liu, S., Ahuja, N., Yang, M. H., & Ghanem, B. (2014b). Robust visual tracking via consistent low-rank sparse learning. International Journal of Computer Vision (IJCV), 111(2), 171–190.CrossRefGoogle Scholar
  71. Zhang, S., Yao, H., Sun, X., & Lu, X. (2013b). Sparse coding based visual tracking: Review and experimental comparison. Pattern Recognition, 46(7), 1772–1788.Google Scholar
  72. Zhong, W., Lu, H., & Yang, M. H. (2012). Robust object tracking via sparsity-based collaborative model. In IEEE Computer Society conference on computer vision and pattern recognition (CVPR) (pp. 1838–1845).Google Scholar
  73. Zhong, W., Lu, H., & Yang, M. H. (2014). Robust object tracking via sparse collaborative appearance model. IEEE Transactions on Image Processing (TIP), 23(5), 2356–68.MathSciNetCrossRefzbMATHGoogle Scholar
  74. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.MathSciNetCrossRefzbMATHGoogle Scholar
  75. Zou, H., Hastie, T., & Tibshirani, R. (2006). Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15(2), 265–286.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Harvard Medical SchoolHarvard UniversityBostonUSA
  2. 2.China Unicom Research InstituteBeijingChina
  3. 3.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  4. 4.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA

Personalised recommendations