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Visual Tracking Using Multi-layer CNN Features Based Discriminant Correlation Filters with Foreground Mask

  • Tao YangEmail author
  • Cindy CappelleEmail author
  • Yassine RuichekEmail author
  • Mohammed El Bagdouri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

This work deals with visual object tracking. The well known discriminant correlation filter (DCF) based approach is improved by multi-layer CNN features, spatial reliability (through a foreground mask) and conditionally model updating strategy. In the training stage, by calculating a foreground mask using the color histograms, for each chosen CNN layer, a correlation filter is trained under the foreground constraint to construct a weak tracker. In next frame, the tracking position is from the weighting of weak trackers, for which the weights are computed by Hedge method. The response peak and oscillation are both considered to estimate the confidence criteria. The model and weight of each weak tracker are updated only when the tracking is high-confident. We analyze and evaluate our system on OTB-13 dataset, and show that our approach performs superiorly against several state-of-the-art methods.

Keywords

Visual tracking Correlation filter Spatial reliability CNN features Hedge method 

Notes

Acknowledgments

The authors gratefully acknowledge financial support from China Scholarship Council.

References

  1. 1.
    Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_56CrossRefGoogle Scholar
  2. 2.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2544–2550. IEEE (2010)Google Scholar
  3. 3.
    Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, 1–5 September 2014. BMVA Press (2014)Google Scholar
  4. 4.
    Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4310–4318 (2015)Google Scholar
  5. 5.
    Galoogahi, H.K., Sim, T., Lucey, S.: Correlation filters with limited boundaries. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4630–4638. IEEE (2015)Google Scholar
  6. 6.
    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. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33765-9_50CrossRefGoogle Scholar
  7. 7.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRefGoogle Scholar
  8. 8.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)CrossRefGoogle Scholar
  9. 9.
    Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16181-5_18CrossRefGoogle Scholar
  10. 10.
    Lukezic, A., Vojir, T., Cehovin, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2 (2017)Google Scholar
  11. 11.
    Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074–3082 (2015)Google Scholar
  12. 12.
    Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., Yang, M.H.: Hedged deep tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4303–4311 (2016)Google Scholar
  13. 13.
    Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 21–26 (2017)Google Scholar
  14. 14.
    Wang, N., Shi, J., Yeung, D.Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3101–3109. IEEE (2015)Google Scholar
  15. 15.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer vision and pattern recognition (CVPR), pp. 2411–2418. IEEE (2013)Google Scholar
  16. 16.
    Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 188–203. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10599-4_13CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Le2i EA7508, CNRS, Arts et Métiers, Univ. Bourgogne Franche-Comté, UTBMBelfortFrance

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