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Robust video tracking algorithm: a multi-feature fusion approach

Robust video tracking algorithm: a multi-feature fusion approach

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This study proposes a novel robust video tracking algorithm consists of target detection, multi-feature fusion, and extended Camshift. Firstly, a novel target detection method that integrates Canny edge operator, three-frame difference, and improved Gaussian mixture model (IGMM)-based background modelling is provided to detect targets. The IGMM-based background modelling divides video frames into meshes to avoid pixel-wise processing. In addition, the output of the target detection is utilised to initialise the IGMM and to accelerate the convergence of iterations. Secondly, low-dimensional regional covariance matrices are introduced to describe video targets by fusing multiple features like pixel location, colour index, rotation and scale invariant features as well as uniform local binary patterns, and directional derivatives. Thirdly, an extended Camshift based on adaptive kernel bandwidth and robust H state estimation is proposed to predict the states of fast moving targets and to reduce the mean shift iterations. Finally, the effectiveness of the proposed tracking algorithm is demonstrated via experiments.

References

    1. 1)
      • 1. Exner, D., Bruns, E., Kurz, D., et al: ‘Fast and robust Camshift tracking’. Proc. Int. Conf. Computer Vision and Pattern Recognition, San Francisco, CA, 2010, pp. 916.
    2. 2)
      • 2. Kim, G.W., Kang, D.S.: ‘Improved Camshift algorithm based on Kalman filter’, Adv. Sci. Technol. Lett., 2015, 98, pp. 135137.
    3. 3)
      • 3. Hsia, C.H., Liou, Y.J., Chiang, J.S.: ‘Directional prediction Camshift algorithm based on adaptive search pattern for moving object tracking’, J. Real-Time Image Process., 2016, 12, (1), pp. 183195.
    4. 4)
      • 4. Wang, Q.X., Li, X.: ‘A New improved Camshift algorithm and its experimental analysis’. Int. Conf. on Electronic Science and Automation Control, 2015, pp. 275280.
    5. 5)
      • 5. Lee, L.K., An, S.Y.: ‘Robust visual object tracking with extended Camshift in complex environments’. Proc. Int. Conf. IEEE Industrial Electronics Society, 2011, pp. 45364542.
    6. 6)
      • 6. Zhang, Y.Y., Zhao, X.M., Li, F.j.: ‘Robust object tracking based on simplified codebook masked Camshift algorithm’. Mathematical Problems in Engineering, 2015, pp. 112.
    7. 7)
      • 7. Yan, Z.G., Liang, W.G., Lv, H.D.: ‘A target tracking algorithm based on improved Camshift and UKF’, J. Softw. Eng. Appl., 2014, 7, pp. 10651073.
    8. 8)
      • 8. Zhang, T., Xu, C.S., Yang, M.H.: ‘Multi-task correlation particle filter for robust object tracking’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, 2017, pp. 43354343.
    9. 9)
      • 9. Zhang, T., Liu, S., Xu, C., et al: ‘Mining semantic context information for intelligent video surveillance of traffic scenes’, IEEE Trans. Ind. Inf., 2013, 9, (1), pp. 149160.
    10. 10)
      • 10. Canny, J.: ‘A computational approach to edge detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, 8, (6), pp. 679698.
    11. 11)
      • 11. Varadarajan, S., Miller, P., Zhou, H.Y.: ‘Region-based mixture of Gaussians modelling for foreground detection in dynamic scenes’, Pattern Recognit., 2015, 48, (11), pp. 34883503.
    12. 12)
      • 12. Bouwmans, T.: ‘Traditional and recent approaches in background modelling for foreground detection: an overview’, Comput. Sci. Rev., 2014, 11, pp. 3136.
    13. 13)
      • 13. Bhosle, P.I., Bijole, L.: ‘Video object segmentation and tracking using GMM and GMM-RBF method for surveillance system’, Int. J. Recent Innov. Trends Comput. Commun., 2015, 3, (6), pp. 35883594.
    14. 14)
      • 14. Tuzel, O., Porikli, F., Meer, P.: ‘Region covariance: a fast descriptor for detection and classification’. Proc. Int. Conf. Computer Vision, Amsterdam, The Netherlands, 2006, vol. 3952, pp. 589600.
    15. 15)
      • 15. Förstner, W., Moonen, B.: ‘A metric for covariance matrices’. Proc. Int. Conf. Challenge of the 3rd Millennium, 2003, pp. 299309.
    16. 16)
      • 16. Wang, H., Nguang, S.K.: ‘Robust video target tracking based on multi-feature fusion and H filtering’, Int. J. Comput. Commun. Eng., 2016, 5, (22), pp. 7997.
    17. 17)
      • 17. Xi, M., Chen, L., Polajnar, D., et al: ‘Local binary pattern network: a deep learning approach for face recognition’. Proc. Int. Conf. Image Processing, Phoenix, USA, 2016, pp. 32243228.
    18. 18)
      • 18. Hong, X., Zhao, G., Pietikäinen, M., et al: ‘Combining LBP difference and feature correlation for texture description’, IEEE Trans. Image Process., 2014, 23, (6), pp. 25572568.
    19. 19)
      • 19. Pietikäinen, M., Zhao, G.: ‘Two decades of local binary patterns: a survey’, in ‘Advances in independent component analysis and learning machines’, (Academic Press, London, 2015), pp. 175210.
    20. 20)
      • 20. Zhao, G., Ahonen, T., Matas, J., et al: ‘Rotation-invariant image and video description with local binary pattern features’, IEEE Trans. Image Process., 2011, 21, (4), pp. 14651477.
    21. 21)
      • 21. Ning, J.F., Zhang, L., Zhang, D.: ‘Scale and orientation adaptive mean shift tracking’, IET Comput. Vis., 2012, 6, (1), pp. 5261.
    22. 22)
      • 22. Yang, H.H.: ‘Research on the detection and tracking of moving target base on Kernel method’, Int. J. Grid Distrib. Comput., 2015, 8, (2), pp. 91100.
    23. 23)
      • 23. Anand, S., Mittal, S., Tuzel, O.: ‘Semi-supervised Kernel mean shift clustering’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (6), pp. 12011215.
    24. 24)
      • 24. Dou, J.F., Li, J.X.: ‘Robust visual tracking based on joint multi-feature histogram by integrating particle filter and mean shift’, Optik, 2015, 126, pp. 14491456.
    25. 25)
      • 25. Wang, H., Nguang, S.K.: ‘Multi-target video tracking based on improved data association and mixed Kalman/H filtering’, IEEE Sens. J., 2016, 15, (21), pp. 76937704.
    26. 26)
      • 26. Simon, D.: ‘Optimal state estimation Kalman, H and nonlinear approaches’ (John Wiley & Sons, Inc., Publication, Australia, 2006).
    27. 27)
      • 27. Saat, S., Nguang, S.K.: ‘Nonlinear H output feedback control with integrator for polynomial discrete-time systems’, Int. J. Robust Nonlinear Control, 2015, 25, (7), pp. 10511065.
    28. 28)
      • 28. Wu, Y., Lim, J., Yang, M.H.: ‘Object tracking benchmark’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (9), pp. 18341848.
    29. 29)
      • 29. Andrews, S.: ‘BGSLibrary: an openCV C + + background subtraction library’. Proc. Int. IX Workshop de Visão Computacional, Rio de Janeiro, Brazil, 2013.
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