A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration

  • Yang Li
  • Jianke ZhuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)


Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.


Visual Tracking Correlation Filter Kernel Learning 


  1. 1.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. TPAMI 33(8), 1619–1632 (2011)CrossRefGoogle Scholar
  2. 2.
    Boddeti, V.N., Kanade, T., Kumar, B.V.: Correlation filters for object alignment. In: CVPR (2013)Google Scholar
  3. 3.
    Chen, D., Yuan, Z., Wu, Y., Zhang, G., Zheng, N.: Constructing adaptive complex cells for robust object tracking. In: ICCV (2013)Google Scholar
  4. 4.
    Danelljan, M., Khan, F.S., Felsberg, M., van de Weijer., J.: Adaptive color attributes for real-time visual tracking. In: CVPR (2014)Google Scholar
  5. 5.
    Dinh, T.B., Vo, N., erard Medioni, G.: Context tracker: Exploring supporters and distracters in unconstrained environments. In: CVPR (2011)Google Scholar
  6. 6.
    D.S.Bolme, B.A.Draper, J.R.Beveridge: Average of synthetic exact filters. In: CVPR (2009)Google Scholar
  7. 7.
    D.S.Bolme, J.R.Beveridge, B.A.Draper, Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: CVPR (2010)Google Scholar
  8. 8.
    Everingham, M., Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classese(voc) challenge. IJCV 88(2), 303–338 (2010)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI (2010)Google Scholar
  10. 10.
    Galoogahi, H.K., Sim, T., Lucey, S.: Multi-channel correlation filters. In: ICCV (2013)Google Scholar
  11. 11.
    Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV (2011)Google Scholar
  12. 12.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  13. 13.
    Henriques, J.F., Carreira, J., Caseiro, R., Batista, J.: Beyond hard negative mining: Efficient detector learning via block-circulant decomposition. In: ICCV (2013)Google Scholar
  14. 14.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. TPAMI (2014)Google Scholar
  15. 15.
    Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR, pp. 1822–1829. Providence, June 2012Google Scholar
  16. 16.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. In: PAMI (2011)Google Scholar
  17. 17.
    Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A., Lopez, A., Felsberg, M.: Coloring action recognition in still images. IJCV 105(3), 205–221 (2013)Google Scholar
  18. 18.
    Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A., Vanrell, M., Lopez, A.: Color attributes for object detection. In: CVPR (2012)Google Scholar
  19. 19.
    Khan, F.S., van de Weijer, J., Vanrell, M.: Modulating shape features by color attention for object recognition. IJCV 98(1), 49–64 (2012)Google Scholar
  20. 20.
    Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV (2011)Google Scholar
  21. 21.
    Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)Google Scholar
  22. 22.
    Lim, J., Ross, D., Lin, R.S., Yang, M.H.: Incremental learning for visual tracking. In: NIPS (2004)Google Scholar
  23. 23.
    Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: CVPR (2011)Google Scholar
  24. 24.
    Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV (2009)Google Scholar
  25. 25.
    Oron, S., Bar-Hillel, A., Levi, D., Avidan, S.: Locally orderless tracking. In: CVPR (2012)Google Scholar
  26. 26.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  27. 27.
    Revaud, J., Douze, M., Cordelia, S., Jgou, H.: Event retrieval in large video collections with circulant temporal encoding. In: CVPR (2013)Google Scholar
  28. 28.
    Gray, R.M.: Toeplitz and circulant matrices: A review. Now Publishers 77(1–3), 125–141 (2006)Google Scholar
  29. 29.
    Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: CVPR (2012)Google Scholar
  30. 30.
    Wang, D., Lu, H., Yang, M.H.: Least soft-thresold squares tracking. In: CVPR. Portland, June 2013Google Scholar
  31. 31.
    Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: NIPS (2013)Google Scholar
  32. 32.
    van de Weijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: Learning color names for real-world applications. TIP 18(7), 1512–1524 (2009)Google Scholar
  33. 33.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: A benchmark. In: CVPR (2013)Google Scholar
  34. 34.
    Zhang, K., Zhang, L., Yang, M.-H.: Real-Time Compressive Tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  35. 35.
    Zhang, L., van der Maaten, L.: Structure preserving object tracking. In: CVPR (2013)Google Scholar
  36. 36.
    Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: CVPR, pp. 1838–1845, Providence, June 2012Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Computer ScienceZhejiang University ZhejiangHangzhouChina

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