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access icon free Adaptive weighted real-time compressive tracking

Many tracking methods often suffer from the drift problems caused by appearance change. Therefore developing a robust online tracker is still a challenging test. Recently, a simple yet effective and efficient tracking algorithm has been proposed by compressive tracking (CT) paradigm to alleviate the drift to some degree. The CT tracker introduced an appearance model based on features extracted from the multi-scale image feature space in the compressed domain. However, the CT tracker may detect the positive sample that is less important because it does not discriminatively consider the sample importance in its learning procedure. In this study, the authors integrate the sample importance into the CT tracker online learning procedure. They also add an efficient feature select method which can choose the most discriminative power weak classifier and employ the co-training criterion into CT tracker to improve the tracking performance. Experiments show that the proposed tracker demonstrates the superior performance in robustness and efficiency than other state-of-the-art trackers.

References

    1. 1)
    2. 2)
    3. 3)
      • 12. Zhang, K., Song, H.: ‘Real-time visual tracking via online weighted multiple instance learning’. Pattern Recognition, 2012.
    4. 4)
    5. 5)
    6. 6)
      • 8. Grabner, H., Grabner, M., Bischof, H.: ‘Real-time tracking via on-line boosting’. BMVC, 2006.
    7. 7)
      • 24. Blum, A., Mitchell, T.: ‘Combining labeled and unlabeled data with co-training’. Proc. 11th Annual Conf. Computational Learning Theory, ACM, 1998.
    8. 8)
      • 15. Zhang, K., Zhang, L., Yang, M.: ‘Real-time object tracking via online discriminative feature selection’, 2013.
    9. 9)
      • 10. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. 2001 IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2001. CVPR 2001 IEEE, 2001.
    10. 10)
      • 16. Mason, L., Baxter, J., Bartlett, P.L., Frean, M.: ‘Functional gradient techniques for combining hypotheses’. Advances in Neural Information Processing Systems, 1999, pp. 221246.
    11. 11)
    12. 12)
      • 25. Everingham, M., Gool, L., Williams, C., Zisserman, A.: ‘Pascal visual object classes challenge results’. Available at http://www.pascal-network.org, 2005.
    13. 13)
    14. 14)
    15. 15)
      • 11. Lu, H., Zhou, Q., Wang, D., Xiang, R.: ‘A co-training framework for visual tracking with multiple instance learning’. 2011 IEEE Int. Conf. Automatic Face & Gesture Recognition and Workshops (FG 2011)IEEE, 2011.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 19. Li, P., Hastie, T.J., Church, K.W.: ‘Very sparse random projections’. Proc. 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, ACM, 2006.
    20. 20)
      • 17. Diaconis, P., Freedman, D.: ‘Asymptotics of graphical projection pursuit’. The annals of statistics, 1984, pp. 793815.
    21. 21)
    22. 22)
      • 13. Zhang, K., Zhang, L, Yang, M.-H.: ‘Real-time compressive tracking’, Computer Vision–Eccv (Springer-Verlag, New York, 2012, pp. 864877).
    23. 23)
    24. 24)
      • 3. Kwon, J., Lee, K.M.: ‘Visual tracking decomposition’. 2010 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) IEEE, 2010.
    25. 25)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2013.0255
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