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access icon free Robust multi-feature visual tracking via multi-task kernel-based sparse learning

  • Author(s): Bin Kang 1 ; Wei-Ping Zhu 2 ; Dong Liang 3
    • View affiliations
  • Source: Volume 11, Issue 12, December 2017, p. 1172 – 1178
    DOI:  10.1049/iet-ipr.2016.1062 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 10/12/2016, Accepted 28/05/2017, Revised 20/04/2017, Published 30/10/2017

Feature selection and fusion is of crucial importance in multi-feature visual tracking. This study proposes a multi-task kernel-based sparse learning method for multi-feature visual tracking. The proposed sparse learning method can discriminate the reliable and unreliable features for optimal multi-feature fusion through using a Fisher discrimination criterion-based multi-objective model to adaptively train the kernel weights of different features such as pixel intensity, edge and texture. To guarantee a robustness of the sparse representation method, a mixed norm is employed in the sparse leaning method to adaptively select correlated particle observations for multi-task sparse reconstruction. Experimental results show that the proposed sparse learning method can achieve a better tracking performance than state-of-the-art tracking methods do.

Inspec keywords: image fusion; learning (artificial intelligence); feature selection; image representation; object tracking

Other keywords: kernel weights; correlated particle observation selection; optimal multifeature fusion; multitask kernel-based sparse learning method; mixed norm; feature selection; sparse representation method; multitask sparse reconstruction; Fisher discrimination criterion-based multiobjective model; robust multifeature visual tracking

Subjects: Sensor fusion; Computer vision and image processing techniques; Optical, image and video signal processing

References

    1. 1)
      • 13. Mei, X., Ling, H., Wu, Y., et al: ‘Minimum error bounded efficient l1 tracker with occlusion detection’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1257–1264.
    2. 2)
      • 43. Henriques, J., Caseiro, R., Martins, P., et al: ‘Exploiting the circulant structure of tracking-by-detection with kernel’. European Conf. Computer Vision (ECCV), 2012, pp. 702–715.
    3. 3)
      • 27. Thiagarajan, J., Ramamurthy, K., Spanias, A.: ‘Multiple kernel sparse representations for supervised and unsupervised learning’, IEEE Trans. Image Process., 2014, 23, (7), pp. 2905–2915.
    4. 4)
      • 8. Ross, D.A., Lim, J., Lin, R.-S., et al: ‘Incremental learning for robust visual tracking’, Int. J. Comput. Vis., 2008, 77, (1), pp. 125–141.
    5. 5)
      • 42. Wang, Q., Chen, F., Xu, W., et al: ‘Object tracking via partial least squares analysis’, IEEE Trans. Image Process., 2012, 21, (10), pp. 4454–4465.
    6. 6)
      • 24. Wang, L., Yan, H., Lv, K., et al: ‘Visual tracking via kernel sparse representation with mutikernel fusion’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (7), pp. 1132–1141.
    7. 7)
      • 21. Jiang, H., Li, J., Wang, D., et al: ‘Multi-feature tracking via adaptive weights’, Neurocomputing, 2016, 207, pp. 189–201.
    8. 8)
      • 1. Sherrah, J., Ristic, B., Redding, N.J.: ‘Particle filter to track multiple people for visual surveillance’, IET Comput. Vis., 2011, 5, (4), pp. 192–200.
    9. 9)
      • 34. Mehmet, G., Ethem, A.: ‘Multiple kernel learning algorithms’, J. Mach. Learn. Res., 2011, 12, pp. 2211–2268.
    10. 10)
      • 25. Ji, Z., Wang, W., Lu, K.: ‘Robust object tracking via multi-task kernel dynamic sparse model’. IEEE Int. Conf. Image Processing (ICIP), 2015.
    11. 11)
      • 45. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005, pp. 886–893.
    12. 12)
      • 40. Wu, Y., Lim, J., Yang, M.-H.: ‘Online object tracking: a benchmark’. Proc. CVPR, 2013, pp. 2411–2418.
    13. 13)
      • 37. Zhang, L., Yang, M., Feng, X.: ‘Sparse representation or collaborative representation: which helps face recognition?’. IEEE Int. Conf. Computer Vision, 2012, pp. 471–478.
    14. 14)
      • 46. Ojala, T., Pietikainen, M., Maepaa, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971–987.
    15. 15)
      • 35. Yang, M., Zhang, L., Feng, X., et al: ‘Sparse representation based Fisher discrimination dictionary learning for image classification’, Int. J. Comput. Vis., 2014, 109, (3), pp. 209–232.
    16. 16)
      • 4. Babenko, B., Ming-Hsuan, Y., Belongie, S.: ‘Visual tracking with online multiple instance learning’. IEEE Conf. Computer Vision and Pattern Recognition, 2009, pp. 983–990.
    17. 17)
      • 6. Kalal, Z., Mikolajczyk, K., Matas, J.: ‘Tracking-learning-detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (7), pp. 1409–1422.
    18. 18)
      • 26. Lan, X., Ma, A.J., Yuan, P.C., et al: ‘Joint sparse representation and robust feature-level fusion for multi-cue visual tracking’, IEEE Trans. Image Process., 2015, 24, (12), pp. 5826–5841.
    19. 19)
      • 5. Zhang, K., Zhang, L., Yang, M.-H.: ‘Fast compressive tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (10), pp. 2002–2015.
    20. 20)
      • 41. Zhang, K., Zhang, L., Yang, M.-H.: ‘Real-time compressive tracking’. European Conf. Computer Vision (ECCV), 2012.
    21. 21)
      • 32. Li, Y., Ngom, A.: ‘Classification approach based on non-negative least squares’, Neurocomputing, 2013, 118, (22), pp. 41–57.
    22. 22)
      • 38. Zhang, T., Ghanem, B., Liu, S., et al: ‘Robust visual tracking via exclusive context modeling’, IEEE Trans. Cybern., 2016, 46, (1), pp. 51–63.
    23. 23)
      • 39. Tseng, P.: ‘On accelerated proximal gradient methods for convex–concave optimization’, SIAM J. Optim., 2008, 1, (1), pp. 1–20.
    24. 24)
      • 15. Zhang, T., Bernard, G., Liu, S., et al: ‘Robust visual tracking via structured multi-task sparse learning’, Int. J. Comput. Vis., 2013, 101, (2), pp. 367–383.
    25. 25)
      • 9. Wu, Y., Shen, B., Ling, H.: ‘Visual tracking via online nonnegative matrix factorization’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (3), pp. 374–383.
    26. 26)
      • 30. Duda, R.D., Hart, P.E., Stork, D.G.: ‘Pattern classification’ (Wiley-Interscience, USA, 2000).
    27. 27)
      • 3. Gustafsson, F., Gunnarsson, F., Bergman, N., et al: ‘Particle filters for positioning, navigation, and tracking’, IEEE Trans. Signal Process., 2002, 50, (2), pp. 425–437.
    28. 28)
      • 31. Yang, M., Zhang, L., Feng, X., et al: ‘Fisher discrimination dictionary learning for sparse representation’. IEEE Int. Conf. Computer Vision (ICCV), 2011.
    29. 29)
      • 44. Hare, S., Saffari, A., Torr, P.H.S.: ‘Struck: structured output tracking with kernels’. IEEE Int. Conf. Computer Vision (ICCV), 2011.
    30. 30)
      • 23. Hu, W., Li, W., Zhang, X., et al: ‘Single and multiple object tracking using a multi-feature joint sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (4), pp. 816–833.
    31. 31)
      • 7. Junseok, K., Kyoung Mu, L.: ‘Visual tracking decomposition’. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
    32. 32)
      • 18. Li, H., Shen, C., Shi, Q.: ‘Real-time visual tracking using compressive sensing’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2011.
    33. 33)
      • 20. Chen, P., Zhang, X., Mao, A., et al: ‘Visual tracking via adaptive multi-task feature learning with calibration and identification’, Signal Process., Image Commun., 2016, 49, pp. 17–24.
    34. 34)
      • 2. Zhu, Z., Ji, Q.: ‘Eye gaze tracking under natural head movements’. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
    35. 35)
      • 11. Zhang, S., Yao, H., Sun, X., et al: ‘Sparse coding base visual tracking: review and experimental comparison’, Pattern Recognit., 2013, 46, (7), pp. 1772–1788.
    36. 36)
      • 22. Hong, Z., Mei, X., Prokhorov, D., et al: ‘Tracking via robust multi-task multi-view joint sparse representation’. IEEE Int. Conf. Computer Vision, 2013, pp. 649–656.
    37. 37)
      • 33. Deb, K., Pratap, A., Agarwal, S., et al: ‘A fast and elitist multiobjective genetic algorithm: NSGA-II’, IEEE Trans. Evol. Comput., 2002, 6, (2), pp. 182–197.
    38. 38)
      • 10. Xie, Y., Zhang, W., Qu, Y., et al: ‘Discriminative subspace learning with sparse representation view-based model for robust visual tracking’, Pattern Recognit., 2014, 47, (3), pp. 1383–1394.
    39. 39)
      • 36. Shrivastava, A., Patel, V.M., Chellappa, R.: ‘Multiple kernel learning for sparse representation-based classification’, IEEE Trans. Image Process., 2014, 23, (7), pp. 3013–3024.
    40. 40)
      • 12. Mei, X., Ling, H.: ‘Robust visual tracking and vehicle classification via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (11), pp. 2259–2272.
    41. 41)
      • 16. Zhang, T., Liu, S., Narendra, A., et al: ‘Robust visual tracking vis consistent low-rank sparse learning’, Int. J. Comput. Vis., 2014, 111, (2), pp. 171–190.
    42. 42)
      • 14. Bao, C., Wu, Y., Ling, H., et al: ‘Real time robust L1 tracker using accelerated proximal gradient approach’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2012, pp. 1830–1837.
    43. 43)
      • 29. Zhang, X., Pham, D.-S., Venkatesh, S., et al: ‘Mixed-norm sparse representation for multi view face recognition’, Pattern Recognit., 2015, 48, (9), pp. 2935–2946.
    44. 44)
      • 19. Wang, D., Lu, H., Bo, C.: ‘Online visual tracking via two view sparse representation’, IEEE Signal Process. Lett., 2014, 21, (9), pp. 1031–1034.
    45. 45)
      • 17. Xiao, Z., Lu, H., Wang, D.: ‘L2-RLS-based object tracking’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (8), pp. 1301–1309.
    46. 46)
      • 28. Gao, S., Tsang, I.W.-H., Chia, L.-T.: ‘Sparse representation with kernels’, IEEE Trans. Image Process., 2013, 22, (2), pp. 423–434.
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