Advertisement

Correlation Filter Tracking with Complementary Features

  • Wei Wang
  • Weiguang Li
  • Mingquan Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Although Correlation Filters (CF) tracking algorithms have inherent capability to tackle various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking based on Correlation Filters, feature is one of the most important factors due to its representation power of target appearance. In this paper, we proposed a new tracking framework by integrating the advantage of complementary features to achieve robust tracking performance. The key issue of this work lies in the fact that different features respond to different tracking challenges, which also applies to deep learning features and hand-craft features. Moreover, for the tracking speed balance, we train a light-weight deep CNN features by using end-to-end learning method, which has the same Parameter magnitude as the hand-crafted features. Experimental results on OTB-2013, OTB-2015 large benchmarks datasets show that the proposed tracker performs favorably against several state-of-the-art methods.

Keywords

Visual tracking End-to-end learning Correlation filters 

References

  1. 1.
    Junseok, K., Leek, M.L.: Visual tracking decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276. IEEE Press, San Francisco (2010)Google Scholar
  2. 2.
    Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: International Conference on Computer Vision, pp. 263–270. IEEE Computer Society (2011)Google Scholar
  3. 3.
    Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)CrossRefGoogle Scholar
  4. 4.
    Bolme, D.S., Beveridge, J.R.: Visual object tracking using adaptive correlation filters. Comput. Vis. Pattern Recognit. 119(5), 2544–2550 (2010)Google Scholar
  5. 5.
    Galoogahi, H. K., Sim, T., Lucey, S.: Multi-channel correlation filters. In: IEEE International Conference on Computer Vision, pp. 3072–3079, IEEE (2014)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.
    Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 809–817. Curran Associates Inc., Lake Tahoe (2013)Google Scholar
  8. 8.
    Zhang, K., Liu, Q., Wu, Y., Yang, M.H.: Robust visual tracking via convolutional networks without training. IEEE Trans. Image Process. 25(4), 1779–1792 (2016)MathSciNetGoogle Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural network. Adv. Neural. Inf. Process. Syst. 60(1), 1097–1105 (2012)Google Scholar
  10. 10.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)Google Scholar
  11. 11.
    Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302. IEEE (2016)Google Scholar
  12. 12.
    Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: IEEE International Conference on Computer Vision, pp. 3119–3127. IEEE (2015)Google Scholar
  13. 13.
    Wang, L., Ouyang, W., Wang, X., Lu, H.: STCT: sequentially training convolutional networks for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1373–1381. IEEE (2016)Google Scholar
  14. 14.
    Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.S.: End-to-end representation learning for correlation filter based tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5000–5008. IEEE (2017)Google Scholar
  15. 15.
    Wang, Q., Gao, J., Xing, J., Zhang, M., Hu, W.: Dcfnet: discriminant correlation filters network for visual tracking. arXiv:2017.1704.04057, http://cn.arxiv.org/abs/1704.04057
  16. 16.
    Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.: Staple: complementary learners for real-time tracking. Comput. Vis. Pattern Recognit. 38(2), 1401–1409 (2016)Google Scholar
  17. 17.
    Zhang, L., Suganthan, P.N.: Robust visual tracking via co-trained kernelized correlation filters. Pattern Recognit. 69(1), 82–93 (2017)CrossRefGoogle Scholar
  18. 18.
    Ma, C., Xu, Y., Ni, B., Yang, X.: When correlation filters meet convolutional neural networks for visual tracking. IEEE Signal Process. Lett. 23(10), 1454–1458 (2016)CrossRefGoogle Scholar
  19. 19.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 119, pp. 2544–2550. IEEE (2010)Google Scholar
  20. 20.
    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 (2014)CrossRefGoogle Scholar
  21. 21.
    Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, vol. 65, pp. 1–11 (2014)Google Scholar
  22. 22.
    Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1387–1395. IEEE (2016)Google Scholar
  23. 23.
    Danelljan, M., Häger, G., Khan, F. S., Felsberg, M.: Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1430–1438. IEEE (2016)Google Scholar
  24. 24.
    Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396. IEEE (2015)Google Scholar
  25. 25.
    Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.: Staple: complementary learners for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 38, no. 2, pp. 1401–1409. IEEE (2015)Google Scholar
  26. 26.
    Zhang, L., Suganthan, P.N.: Robust visual tracking via co-trained kernelized correlation filters. Pattern Recognit. 69, 82–93 (2017)CrossRefGoogle Scholar
  27. 27.
    Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: IEEE International Conference on Computer Vision, pp. 3074–3082. IEEE Computer Society (2015)Google Scholar
  28. 28.
    Zhang, L., Varadarajan, J., Suganthan, P.N., Ahuja, N., Moulin, P.: Robust visual tracking using oblique random forests. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5825–5834. IEEE (2017)Google Scholar
  29. 29.
    Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Eco: efficient convolution operators for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6931–6939. IEEE (2017)Google Scholar
  30. 30.
    Bertinetto, L., Valmadre, J., Henriques, João F., Vedaldi, A., Torr, Philip 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
  31. 31.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv 2014.1409.1556. http://cn.arxiv.org/abs/1409.1556
  32. 32.
    Russakovsky, O., Deng, J., Su, H.: imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)CrossRefGoogle Scholar
  34. 34.
    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 Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations