Extended cuckoo search-based kernel correlation filter for abrupt motion tracking

Extended cuckoo search-based kernel correlation filter for abrupt motion tracking

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Kernelised correlation filter (KCF)-based trackers have recently attracted considerable attention due to their exciting accuracy and efficiency. Numerous improvements have been made later for coping with scales variation or partial occlusion etc. However, when there is an abrupt motion between the consecutive image frames, these trackers would face failure. To alleviate the problem, the authors present an extended cuckoo search (CS)-based KCF tracker (called ECSKCF). At first, the extended CS algorithm is constructed by the Simplex method (SM). CS has obvious capability in global search while the SM has exceptional advantage in local search. Based on ECS method, motion prediction is transformed to globally search for optimal position intending to enhance the quality of base image. Then, combined ECS with Gaussian distribution, a hybrid motion model is introduced to KCF framework, which has the capability of capturing abrupt motion. Finally, a unified framework is designed to track smooth or abrupt motion simultaneously. Extensive experimental results in both quantitative and qualitative measures demonstrate the effectiveness of the authors’ proposed method for abrupt motion tracking.


    1. 1)
      • 1. Zhou, T., Harish, B., Liu, F., et al: ‘Graph regularized and locality-constrained coding for robust visual tracking’, IEEE Trans. Control Netw. Syst., 2016, 27, (10), pp. 11.
    2. 2)
      • 2. Hanif, S., Ahmad, S., Khurshid, K.: ‘On the improvement of foreground background model-based object tracker’, IET Comput. Vis., 2017, 11, (6), pp. 488496.
    3. 3)
      • 3. Zhang, H., Hu, S., Zhang, X., et al: ‘Visual tracking via constrained incremental nonnegative matrix factorization’, IEEE Signal Process. Lett., 2015, 22, (9), pp. 13501353.
    4. 4)
      • 4. Zhang, H., Wang, Y., Luo, L., et al: ‘SIFT flow for abrupt motion tracking via adaptive samples selection with sparse representation’, Neurocomputing, 2017, 249, (2), pp. 253265.
    5. 5)
      • 5. Wang, D., Lu, H.: ‘Fast and robust object tracking via probability continuous outlier model’, IEEE Trans. Image Process., 2015, 24, (12), pp. 51665176.
    6. 6)
      • 6. Ning, J., Yang, J., Jiang, S., et al: ‘Object tracking via dual linear structured SVM and explicit feature map’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016, pp. 42664274.
    7. 7)
      • 7. Gu, D., Huang, Y., Tokuta, A.: ‘Multiscale spatially regularised correlation filters for visual tracking’, IET Comput. Vis., 2017, 11, (3), pp. 220225.
    8. 8)
      • 8. Zhang, H., Zhang, J., Wu, Q., et al: ‘Extended kernel correlation filter for abrupt motion tracking’, KSII Trans. Internet Inf. Syst., 2017, 11, (9), pp. 44384460.
    9. 9)
      • 9. Bolme, D.S., Beveridge, J.R., Draper, B.A., et al: ‘Visual object tracking using adaptive correlation filters’. Proc. of 2010 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, USA, August 2010, pp. 25442550.
    10. 10)
      • 10. Henriques, J., Caseiro, R., Martins, P., et al: ‘Exploiting the circulant structure of tracking-by-detection with kernels’. Proc. of the 12th European Conf. on Computer Vision, Berlin, Germany, 2012, pp. 702715.
    11. 11)
      • 11. Henriques, J.F., Caseiro, R., Martins, P., et al: ‘High-speed tracking with Kernelized correlation filters’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (3), pp. 583596.
    12. 12)
      • 12. Danelljan, M., Hager, G., Khan, F., et al: ‘Accurate scale estimation for robust visual tracking’. British Machine Vision Conf., Nottingham, UK, 2015, pp. 65.165.11.
    13. 13)
      • 13. Xu, Y., Wang, J., Li, H., et al: ‘Patch-based scale calculation for real-time visual tracking’, IEEE Signal Process. Lett., 2016, 23, (1), pp. 4044.
    14. 14)
      • 14. Zhu, G., Wang, J., Wu, Y., et al: ‘Collaborative correlation tracking’. Proc. of the British Machine Vision Conf. (BMVC), Nottingham, UK, September 2015, vol. 2, no. 4, pp. 184.1184.12.
    15. 15)
      • 15. Liu, T., Wang, G., Yang, Q.: ‘Real-time part-based visual tracking via adaptive correlation filter’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015, pp. 49024912.
    16. 16)
      • 16. Liu, S., Zhang, T., Cao, X., et al: ‘Structural correlation filter for robust visual tracking’. IEEE Conf.on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016, pp. 43124320.
    17. 17)
      • 17. Mueller, M., Smith, N., Ghanem, B.: ‘Context-aware correlation filter tracking’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Hawaii, HI, USA, July 2017, pp. 13871395.
    18. 18)
      • 18. Ma, C., Huang, J.-B., Yang, X., et al: ‘Hierarchical convolutional features for visual tracking’. IEEE Int. Conf. on Computer Vision (ICCV), Santiago, Chile, December 2015, pp. 30743082.
    19. 19)
      • 19. Zhang, T., Xu, C., Yang, M.H.: ‘Multi-task correlation particle filter for robust object tracking’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017.
    20. 20)
      • 20. Li, Y., Zhang, Y., Xu, Y., et al: ‘Robust scale adaptive kernel correlation filter tracker with hierarchical convolutional features’, IEEE Signal Process., 2016, 23, (8), pp. 11361140.
    21. 21)
      • 21. Li, W., Zhang, X., Hu, W.: ‘Contour tracking with abrupt motion’. Int. Conf. of Image Processing (ICIP), Cairo, Egypt, 2009, pp. 35933596.
    22. 22)
      • 22. Zhang, X., Hu, W., Maybank, S., et al: ‘Sequential particle swarm optimization for visual tracking’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA, 2008, pp. 18.
    23. 23)
      • 23. Lim, M., Chan, C., Monekosso, D., et al: ‘Swatrack: a swarm intelligence-based abrupt motion tracker’. Proc. of IAPR MVA, 2013, pp. 3740.
    24. 24)
      • 24. Gao, M., Shen, J., Yin, L., et al: ‘A novel visual tracking method using bat algorithm’, Neurocomputing, 2016, 177, pp. 612619.
    25. 25)
      • 25. Nelder, J.A., Mead, R.: ‘A Simplex method for function minimization’, Comput. J., 1965, 7, (4), pp. 308313.
    26. 26)
      • 26. Zahara, E., Kao, Y.T.: ‘Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems’, Expert Syst. Appl., 2009, 36, (2), pp. 38803886.
    27. 27)
      • 27. Davoodi, E., Hagh, M.T., Zadeh, S.G.: ‘A hybrid improved quantum-behaved particle swarm optimization-simplex method (IQPSOS) to solve power system load flow problems’, Appl. Soft Comput., 2014, 21, (2), pp. 171179.
    28. 28)
      • 28. Yang, X.S., Deb, S.: ‘Cuckoo search via levy flights’. Nature Biologically Inspired Computing (NaBIC), Coimbatore, India, 2009, pp. 210214.
    29. 29)
      • 29. Zhang, K., Zhang, L., Yang, M.: ‘Fast compressive tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (10), pp. 20022015.
    30. 30)
      • 30. Zhang, K., Zhang, L., Liu, Q., et al: ‘Fast visual tracking via dense spatiotemporal context learning’. Proc. of the European Conf. on Computer Vision, Zürich, Switzerland, 2014, pp. 127141.
    31. 31)
      • 31. Wang, D., Lu, H., Yang, M.: ‘Robust visual tracking via least soft-threshold squares’, IEEE Trans. Circuits Syst. Video Technol., 2016, 26, (9), pp. 17091721.
    32. 32)
      • 32. Li, Y., Zhu, J., Hoi, S.C.H.: ‘Reliable patch trackers: robust visual tracking by exploiting reliable patches’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 353361.
    33. 33)
      • 33. Wu, Y., Lee, J.W., Yang, M.H.: ‘Object tracking benchmark’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (9), pp. 18341848.

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