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Adaptive Learning Rate and Spatial Regularization Background Perception Filter for Visual Tracking

  • Conference paper
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Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

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Abstract

In recent years, the correlation filter (CF) has excellent accuracy and speed in the field of visual object tracking. Training samples for CF are usually generated by circular shifts. Although such training samples combined with Fourier transform can be effective in reducing computational effort. They also give rise to boundary effects. Spatial regularization can effectively suppress the boundary effect, but the learning rate are fixed. They cannot be adaptively adjusted to match environmental changes, and the background information is not suppressed. In this paper, we propose a new Correlation filter model, namely Adaptive Learning Rate and Spatial Regularization Background Perception Filter for Visual Tracking (SRAL). Firstly, the SRAL uses real background information as negative samples to train the filter model. Secondly, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames to adjust the learning rate together. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.

This work was supported by the National Natural Science Foundation of China [grant number U1813220] and the Natural Science Foundation of Xinjiang Uygur Autonomous Region [grant number 2019D01C02].

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References

  1. Gao, M., Jin, L., Jiang, Y., Guo, B.: Manifold Siamese network: a novel visual tracking convnet for autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 21(4), 1612–1623 (2020)

    Article  Google Scholar 

  2. Zg, A., Gz, A., Hd, B., Xy, A.: Extended geometric models for stereoscopic 3D with vertical screen disparity. Displays 65, 101972 (2020)

    Article  Google Scholar 

  3. Manafifard, M., Ebadi, H., Moghaddam, H.A.: A survey on player tracking in soccer videos. Comput. Vis. Image Underst. 159, S1077314217300309 (2017)

    Article  Google Scholar 

  4. Bouget, D., Allan, M., Stoyanov, D., Jannin, P.: Vision-based and marker-less surgical tool detection and tracking: a review of the literature. Med. Image Anal. 35, 633–654 (2017)

    Article  Google Scholar 

  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 (2015)

    Google Scholar 

  6. Galoogahi, H.K., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1144–1152 (2017)

    Google Scholar 

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

    Google Scholar 

  8. LukeŽic, A., Vojír, T., Zajc, L.C., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4847–4856 (2017)

    Google Scholar 

  9. Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4800–4808 (2017)

    Google Scholar 

  10. Boyd, S., Parikh, N., Chu, E.: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Now Publishers Inc., Norwell (2011)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  12. Liang, P., Blasch, E., Ling, H.: Encoding color information for visual tracking: algorithms and benchmark. IEEE Trans. Image Process. 24(12), 5630–5644 (2015)

    Article  MathSciNet  Google Scholar 

  13. Kristan, M., et al.: The sixth visual object tracking VOT2018 challenge results. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 3–53. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11009-3_1

    Chapter  Google Scholar 

  14. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010)

    Google Scholar 

  15. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005)

    Google Scholar 

  16. van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)

    Article  MathSciNet  Google Scholar 

  17. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. Computer Science (2014)

    Google Scholar 

  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)

    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. Danelljan, M., Khan, F.S., Felsberg, M., Van De Weijer, J.: Adaptive color attributes for real-time visual tracking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090–1097 (2014)

    Google Scholar 

  21. Ma, C., Huang, J., Yang, X., Yang, M.: Hierarchical convolutional features for visual tracking. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3074–3082 (2015)

    Google Scholar 

  22. 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 

  23. 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 

  24. Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017). https://doi.org/10.1109/TPAMI.2016.2609928

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

    Google Scholar 

  26. Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.: Learning spatial-temporal regularized correlation filters for visual tracking. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4904–4913 (2018)

    Google Scholar 

  27. Dai, K., Wang, D., Lu, H., Sun, C., Li, J.: Visual tracking via adaptive spatially-regularized correlation filters. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4665–4674 (2019)

    Google Scholar 

  28. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Eco: efficient convolution operators for tracking. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6931–6939 (2017)

    Google Scholar 

  29. Huang, Z., Fu, C., Li, Y., Lin, F., Lu, P.: Learning aberrance repressed correlation filters for real-time UAV tracking. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2891–2900 (2019)

    Google Scholar 

  30. Li, Y., Fu, C., Ding, F., Huang, Z., Lu, G.: AutoTrack: towards high-performance visual tracking for UAV with automatic spatio-temporal regularization. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11920–11929 (2020)

    Google Scholar 

  31. He, Z., Fan, Y., Zhuang, J., Dong, Y., Bai, H.: Correlation filters with weighted convolution responses. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1992–2000 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Xinjiang University, Urumqi, China

    Kai Lv, Liang Yuan & L. He

  2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China

    Liang Yuan & Ran Huang

  3. School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China

    Jie Mei

Authors
  1. Kai Lv
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  2. Liang Yuan
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  3. L. He
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  4. Ran Huang
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  5. Jie Mei
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Corresponding author

Correspondence to Liang Yuan .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Lv, K., Yuan, L., He, L., Huang, R., Mei, J. (2021). Adaptive Learning Rate and Spatial Regularization Background Perception Filter for Visual Tracking. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_57

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_57

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-93046-2

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