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Efficient Multi-level Correlating for Visual Tracking

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11365))

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Abstract

Correlation filter (CF) based tracking algorithms have demonstrated favorable performance recently. Nevertheless, the top performance trackers always employ complicated optimization methods which constrain their real-time applications. How to accelerate the tracking speed while retaining the tracking accuracy is a significant issue. In this paper, we propose a multi-level CF-based tracking approach named MLCFT which further explores the potential capacity of CF with two-stage detection: primal detection and oriented re-detection. The cascaded detection scheme is simple but competent to prevent model drift and accelerate the speed. An effective fusion method based on relative entropy is introduced to combine the complementary features extracted from deep and shallow layers of convolutional neural networks (CNN). Moreover, a novel online model update strategy is utilized in our tracker, which enhances the tracking performance further. Experimental results demonstrate that our proposed approach outperforms the most state-of-the-art trackers while tracking at speed of exceeded 16 frames per second on challenging benchmarks.

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References

  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_56

    Chapter  Google Scholar 

  2. 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 (CVPR), pp. 2544–2550 (2010)

    Google Scholar 

  3. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)

  4. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: efficient convolution operators for tracking. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26 (2017)

    Google Scholar 

  5. Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference (BMVC) (2014)

    Google Scholar 

  6. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: International Conference on Computer Vision Workshops (ICCVW), pp. 58–66 (2015)

    Google Scholar 

  7. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: International Conference on Computer Vision, pp. 4310–4318 (2015)

    Google Scholar 

  8. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking. In: Computer Vision and Pattern Recognition, pp. 1430–1438 (2016)

    Google Scholar 

  9. Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_29

    Chapter  Google Scholar 

  10. Galoogahi, H.K., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26 (2017)

    Google Scholar 

  11. Gao, P., et al.: Adaptive object tracking with complementary models. IEICE Trans. Inf. Syst. E101-D(11), 2849–2854 (2018)

    Google Scholar 

  12. Gao, P., Ma, Y., Song, K., Li, C., Wang, F., Xiao, L.: A complementary tracking model with multiple features. arXiv preprint arXiv:1804.07459 (2018)

  13. Gao, P., Ma, Y., Song, K., Li, C., Wang, F., Xiao, L.: Large margin structured convolution operator for thermal infrared object tracking. In: International Conference on Pattern Recognition (ICPR), pp. 2380–2385 (2018)

    Google Scholar 

  14. Gao, P., et al.: High performance visual tracking with circular and structural operators. Knowl.-Based Syst. 161, 240–253 (2018)

    Article  Google Scholar 

  15. 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 (CVPR), pp. 580–587 (2014)

    Google Scholar 

  16. Gundogdu, E., Alatan, A.: Good features to correlate for visual tracking. arXiv preprint arXiv:1704.06326 (2017)

  17. He, Z., Fan, Y., Zhuang, J., Dong, Y., Bai, H.: Correlation filters with weighted convolution responses. In: IEEE International Conference on Computer Vision (ICCV). IEEE (2017)

    Google Scholar 

  18. 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_50

    Chapter  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732 (2014)

    Google Scholar 

  21. Kristan, M., et al.: The visual object tracking VOT2017 challenge results. In: International Conference on Computer Vision (ICCV), pp. 1949–1972 (2016)

    Google Scholar 

  22. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  23. 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_18

    Chapter  Google Scholar 

  24. Liu, Q., Lu, X., He, Z., Zhang, C., Chen, W.S.: Deep convolutional neural networks for thermal infrared object tracking. Knowl.-Based Syst. 134, 189–198 (2017)

    Article  Google Scholar 

  25. Lukežič, A., Vojíř, T., Čehovin, L., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4847–4856. IEEE (2017)

    Google Scholar 

  26. Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)

    Google Scholar 

  27. Qi, Y., et al.: Hedged deep tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4303–4311 (2016)

    Google Scholar 

  28. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)

  29. Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W., Yang, M.H.: CREST: convolutional residual learning for visual tracking. In: International Conference on Computer Vision (ICCV), pp. 2574–2583 (2017)

    Google Scholar 

  30. Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1420–1429 (2016)

    Google Scholar 

  31. Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5000–5008 (2017)

    Google Scholar 

  32. Vedaldi, A., Lenc, K.: MatConvnet: convolutional neural networks for MATLAB. In: ACM International Conference on Multimedia (ACMMM), pp. 689–692 (2015)

    Google Scholar 

  33. Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26 (2017)

    Google Scholar 

  34. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418 (2013)

    Google Scholar 

  35. Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)

    Article  Google Scholar 

  36. Zhang, T., Xu, C., Yang, M.H.: Multi-task correlation particle filter for robust object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

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Acknowledgements

This work is supported by the NSFC project under Grant No. U1833101, Shenzhen Science and Technologies project under Grant No. JCYJ20160428182137473, the Science and Technology Planning Program of Guangdong Province under Grant No. 2016B090918047, and the Joint Research Center of Tencent & Tsinghua.

The authors would like to thank all the anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Yipeng Ma, Peng Gao & Fei Wang

  2. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Chun Yuan

Authors
  1. Yipeng Ma
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  2. Chun Yuan
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  3. Peng Gao
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  4. Fei Wang
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Corresponding authors

Correspondence to Chun Yuan or Fei Wang .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Ma, Y., Yuan, C., Gao, P., Wang, F. (2019). Efficient Multi-level Correlating for Visual Tracking. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_29

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  • DOI: https://doi.org/10.1007/978-3-030-20873-8_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20872-1

  • Online ISBN: 978-3-030-20873-8

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