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Real-Time Tracking with Multi-center Kernel Correlation Filter

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 874))

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Abstract

Recently, visual object tracking based on kernel correlation filtering has achieved great success. Application of robust feature, such as the Histogram of Oriented Gradients, is an important reason for the success of the kernel correlation filtering. However, the extraction of the HOG feature may bias the estimation of the target. To overcoming such kind of deviation, this paper proposes a real-time tracker with a multi-center strategy based on the kernel correlation filtering. Finally, abundant experimental results show that the multi-center kernel correlation filtering tracker of this paper has been made great progress relative the kernel correlation filtering tracker.

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References

  1. Abdechiri, M., Faez, K., Amindavar, H.: Visual object tracking with online weighted chaotic multiple instance learning. Neurocomputing 247, 16–30 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  3. Chen, W., Zhang, K., Liu, Q.: Robust visual tracking via patch based kernel correlation filters with adaptive multiple feature ensemble. Neurocomputing 214, 607–617 (2016)

    Article  Google Scholar 

  4. Chen, Z., Hong, Z., Tao, D.: An experimental survey on correlation filter-based tracking. Comput. Sci. 53(6025), 68–83 (2015)

    Google Scholar 

  5. Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561 (2016)

    Article  Google Scholar 

  6. Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, pp. 65.1–65.11 (2014)

    Google Scholar 

  7. Felzenszwalb, P.F., Girshick, R.B., Mcallester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627 (2010)

    Article  Google Scholar 

  8. 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 

  9. Henriques, J.F., Rui, C., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583 (2015)

    Article  Google Scholar 

  10. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: European Conference on Computer Vision, pp. 254–265 (2014)

    Google Scholar 

  11. Lu, H., Jia, X., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1822–1829 (2012)

    Google Scholar 

  12. Rifkin, R., Yeo, G., Poggio, T.: Regularized least-squares classification. Acta Electronica Sin. 190(1), 93–104 (2003)

    Google Scholar 

  13. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  14. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2013)

    Google Scholar 

  15. Smola, A.J.: Learning with kernels-support vector machines. Lect. Notes Comput. Sci. 42(4), 1–28 (2008)

    Google Scholar 

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

    Article  Google Scholar 

  17. Zhang, L., Suganthan, P.N.: Robust visual tracking via co-trained kernelized correlation filters. Pattern Recognit. 69, 82–93 (2017)

    Article  Google Scholar 

Download references

Acknowledgment

This research was supported by the National Natural Science Foundation of China [grant number 61762055]; the Jiangxi Provincial Natural Science Foundation of China [grant number 20161BAB202036]; and the Jiangxi Provincial Social Science “13th Five-Year” Planning Project of China [grant number 16JY19].

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Authors and Affiliations

  1. Basic Teaching Department, Gongqing Institute of Science and Technology, Gongqingcheng, China

    Taoe Wu

  2. School of Information Science and Technology, Jiujiang University, Jiujiang, China

    Zhiqiang Zhao, Zongmin Cui, Anyuan Deng & Xiao Yang

Authors
  1. Taoe Wu
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  2. Zhiqiang Zhao
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  3. Zongmin Cui
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  4. Anyuan Deng
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  5. Xiao Yang
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Corresponding author

Correspondence to Zongmin Cui .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Wu, T., Zhao, Z., Cui, Z., Deng, A., Yang, X. (2018). Real-Time Tracking with Multi-center Kernel Correlation Filter. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_45

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  • DOI: https://doi.org/10.1007/978-981-13-1651-7_45

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1650-0

  • Online ISBN: 978-981-13-1651-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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