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A new TLD target tracking method based on improved correlation filter and adaptive scale

  • Xin YangEmail author
  • Songyan Zhu
  • Sijun Xia
  • Dake Zhou
Original Article
  • 35 Downloads

Abstract

Target tracking is a popular but challenging problem in computer vision field. Due to many disturbing factors such as position transformation, illumination, and occlusion, it is difficult to achieve continuous target tracking. On the basis of the above analyses, a novel target tracking method based on correlation filters is proposed in this paper. This method uses the improved Tracking–Learning–Detection (TLD) tracking framework which combines the tracker with the detector through the learning mechanism. In the TLD tracking framework, the Spatially Regularized Discriminatively Correlation Filters tracker is used and improved. In addition, the adaptive tracking scale is realized according to the confidence of the searching area. The experimental results show that the proposed algorithm can effectively deal with the attitude change and the illumination problem so that it has better robustness and stability for target continuous tracking.

Keywords

Target tracking TLD SRDCF Correlation filter 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (61573182).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

References

  1. 1.
    Hong, S., Wang, L., Shi, Z.G., et al.: Simplified particle PHD filter for multiple-target tracking: algorithm and architecture[J]. Prog. Electromagn. Res. 120, 481–498 (2011)CrossRefGoogle Scholar
  2. 2.
    Denman, S., Chandran, V., Sridharan, S.: An adaptive optical flow technique for person tracking systems [J]. Pattern Recogn. Lett. 28(10), 1232–1239 (2007)CrossRefGoogle Scholar
  3. 3.
    Mahmoudi, S.A., Kierzynka, M., Manneback, P., et al.: Real-time motion tracking using optical flow on multiple GPUs[J]. Bull. Pol. Acad. Sci. Tech. Sci. 62(1), 139–150 (2014)Google Scholar
  4. 4.
    Zhao, Y., Pei, H., Liu, B.: Meanshift algorithm based on kernel bandwidth adaptive adjust. In: 32nd Chinese Control Conference (CCC), pp. 4486–4490 (2013)Google Scholar
  5. 5.
    Vojir, T., Noskova, J., Matas, J.: Robust scale-adaptive mean-shift for tracking [J]. Pattern Recogn. Lett. 49(C), 250–258 (2014)CrossRefGoogle Scholar
  6. 6.
    Hu, W., Gao, J., Wang, Y., et al.: Online adaboost-based parameterized methods for dynamic distributed network intrusion detection[J]. IEEE Trans. Cybern. 44(1), 66–82 (2014)CrossRefGoogle Scholar
  7. 7.
    Kalal, Z., Matas, J., Mikolajczyk, K.: Online learning of robust object detectors during unstable tracking[C]. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, pp. 1417–1424 (2009)Google Scholar
  8. 8.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking–learning–detection[M]. IEEE Comput. Soc. 34, 1409–1422 (2012)Google Scholar
  9. 9.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures[C]. In: 2010 20th International Conference on Pattern Recognition (ICPR), IEEE, pp. 2756–2759 (2010)Google Scholar
  10. 10.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Face-tld: tracking–learning–detection applied to faces[C]. In: 2010 17th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 3789–3792 (2010)Google Scholar
  11. 11.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., et al.: Visual object tracking using adaptive correlation filters[C]. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 2544–2550 (2010)Google Scholar
  12. 12.
    Henriques, J.F., Caseiro, R., Martins, P., et al.: Exploiting the circulant structure of tracking-by-detection with kernels[C]. In: European Conference on Computer Vision, pp. 702–715. Springer, Berlin, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters[J]. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRefGoogle Scholar
  14. 14.
    Danelljan, M., Häger, G., Khan, F., et al.: Accurate scale estimation for robust visual tracking[C]. In: British Machine Vision Conference, Nottingham, September 1–5, BMVA Press (2014)Google Scholar
  15. 15.
    Danelljan, M., Hager, G., Shahbaz Khan, F., et al.: Learning spatially regularized correlation filters for visual tracking[C]. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 4310–4318 (2015)Google Scholar
  16. 16.
    Zhang, H., Liu, G.: Coupled-layer based visual tracking via adaptive kernelized correlation filters [J]. Vis. Comput. 34(1), 41–54 (2018)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zhang, T., Liu, S., Xu, C., et al.: Correlation particle filter for visual tracking[J]. IEEE Trans. Image Process. 27(99), 2676–2687 (2018)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Valmadre, J., Bertinetto, L., Henriques, J., et al.: End-to-end representation learning for correlation filter based tracking[C]. In: Computer Vision and Pattern Recognition. IEEE, pp. 5000–5008 (2017)Google Scholar
  19. 19.
    Zhang, D., Zhang, Z., Zou, L., et al.: Part-based visual tracking with spatially regularized correlation filters[J]. Vis. Comput. 2019(4)Google Scholar
  20. 20.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2411–2418 (2013)Google Scholar
  21. 21.
    Wu, Y., Lim, J., Yang, M.-H.: Object tracking benchmark. In: PAMI (2015)Google Scholar
  22. 22.
    Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J.: The visual object tracking VOT2014 challenge results. In: Proceedings of European Conference on Computer Vision Workshop Visual Object Tracking Challenge, pp. 191–217 (2014)Google Scholar
  23. 23.
    Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Conference ComputingGoogle Scholar
  24. 24.
    Hare, S., Saffari, A., Torr, P.: Struck: structured output tracking with kernels. In: Proceedings of International Conference on Computing Vision, pp. 263–270 (2011)Google Scholar
  25. 25.
    Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: Proceedings of IEEE Conference ComputingGoogle Scholar
  26. 26.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: Bootstrapping binary classifiers by structural constraints. In: Proceedings of IEEE Conference Computing Vision Pattern Recognition, pp. 49–56 (2010)Google Scholar
  27. 27.
    Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: Proceedings of IEEE Conference Computing Vision Pattern Recognition, pp. 1838–1845 (2012)Google Scholar
  28. 28.
    Henriques, J., 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
  29. 29.
    Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Proceedings of European Conference on Computer Vision Workshop Visual Object Tracking Challenge, pp. 254–265 (2014)Google Scholar
  30. 30.
    Danelljan, M., Hager, G., Khan, F.S., et al.: Discriminative scale space tracking [J]. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)CrossRefGoogle Scholar
  31. 31.
    Danelljan, M., Khan, F.S., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: CVPR (2014)Google Scholar
  32. 32.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection[C]. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp. 886–893 (2005)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Jiangsu College of Engineering and TechnologyNantongChina

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