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Fast compressive tracking combined with Kalman filter

  • Jinguang Chen
  • Xiaoxing Li
  • Mingming Wang
  • Lili Ma
  • Bugao XuEmail author
Article
  • 18 Downloads

Abstract

Compressive tracking refers to a group of high-speed algorithms for real-time object tracking. Many tracking algorithms may not generate accurate tracking results because they used fixed learning rates, and sometime lose targets when objects are occluded or deformed. To address these problems, a fast tracking algorithm combined with Kalman filter was proposed in this research. Firstly, an object location was initialized by the predicted value of Kalman filter when it was occluded, and the Kalman update was implemented only when the object was detected. The object location obtained in the Kalman update stage was used later as the initial position in the next frame. Secondly, when the distribution of positive samples satisfied a threshold, an adaptive learning rate was then updated. Finally, the naive Bayes classifier was updated with samples which had more different features. In the experiment, the proposed algorithm was compared with other state-of-the-art algorithms on seven publicly tested sequences, demonstrating that it had higher tracking accuracy and robustness in conditions such as occlusion, deformation and rotation.

Keywords

Compressive tracking Kalman filter Secondary localization Object tracking 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (61601358), the Natural Science Basic Research Plan in Shaanxi Province of China (2016JM6030), the Scientific Research Program funded by Shaanxi Provincial Education Department (18JK0349).

Compliance with ethical standards

Conflicts of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

References

  1. 1.
    Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632.  https://doi.org/10.1109/TPAMI.2010.226 CrossRefGoogle Scholar
  2. 2.
    Bao C, Wu Y, Ling H et al. (2012) Real time robust L1 tracker using accelerated proximal gradient approach. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1830–1837. doi: https://doi.org/10.1109/CVPR.2012.6247881
  3. 3.
    Bolme SD, Beveridge RJ, Draper AB, Lui MY (2010) Visual object tracking using adaptive correlation filters. IEEE Conf Comput Vision Pattern Recognition, San Francisco, CA, USA: 2544–2550. doi: https://doi.org/10.1109/CVPR.2010.5539960
  4. 4.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577.  https://doi.org/10.1109/TPAMI.2003.1195991 CrossRefGoogle Scholar
  5. 5.
    Danelljan M, Häger G, Khan SF, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575.  https://doi.org/10.1109/TPAMI.2016.2609928 CrossRefGoogle Scholar
  6. 6.
    Dinh TB, Vo N, Medioni G (2011) Context tracker: exploring supporters and distracters in unconstrained environments. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1177–1184. doi: https://doi.org/10.1109/CVPR.2011.5995733
  7. 7.
    Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306.  https://doi.org/10.1109/TIT.2006.871582 MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Han D, Lee J, Lee J et al. A low-power deep neural network online learning processor for real-time object tracking application. IEEE Transactions on Circuits and Systems I: Regular Papers Online in advance. doi: https://doi.org/10.1109/TCSI.2018.2880363
  9. 9.
    He Z, Yi S, Cheung Y, You X et al (2017) Robust object tracking via key patch sparse representation. IEEE Trans Cybernet 47(2):354–364.  https://doi.org/10.1109/TCYB.2016.2514714 CrossRefGoogle Scholar
  10. 10.
    Huang S, Hong J (2011) Moving object tracking system based on Camshift and Kalman filter. International Conference on Consumer Electronics, Communications and Networks XianNing, China, pp 1423–1426. doi: https://doi.org/10.1109/CECNET.2011.5769081
  11. 11.
    Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. IEEE Conference of Computer Vision Pattern Recognition, Providence, RI, USA: 1822–1829. doi: https://doi.org/10.1109/CVPR.2012.6247880
  12. 12.
    Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422.  https://doi.org/10.1109/TPAMI.2011.239 CrossRefGoogle Scholar
  13. 13.
    Kim H, Park R (2018) Residual LSTM attention network for object tracking. IEEE Signal Process Letters 25(7):1029–1033.  https://doi.org/10.1109/LSP.2018.2835768 CrossRefGoogle Scholar
  14. 14.
    Liu Q, Yang J, Zhang K et al (2016) Adaptive compressive tracking via online vector boosting feature selection. IEEE Trans Cybernet PP(99):1–13.  https://doi.org/10.1109/TCYB.2016.2606512 CrossRefGoogle Scholar
  15. 15.
    Liu Q, Hu G, Islam MM (2018) Fast visual tracking with robustifying kernelized correlation filters. IEEE Access 6:43302–43314.  https://doi.org/10.1109/ACCESS.2018.2861827 CrossRefGoogle Scholar
  16. 16.
    Mei X, Ling HB (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272.  https://doi.org/10.1109/TPAMI.2011.66 CrossRefGoogle Scholar
  17. 17.
    Nai K, Li Z, Li G et al (2018) Robust object tracking via local sparse appearance model. IEEE Trans Image Process 27(10):4958–4970.  https://doi.org/10.1109/TIP.2018.2848465 MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Oron S, Bar-Hillel A, Levi D et al. (2012) Locally orderless tracking. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1940–1947. doi: https://doi.org/10.1109/CVPR.2012.6247895
  19. 19.
    Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149.  https://doi.org/10.1109/TPAMI.2016.2577031 CrossRefGoogle Scholar
  20. 20.
    Ross AD, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77:125–141.  https://doi.org/10.1007/s11263-007-0075-7 CrossRefGoogle Scholar
  21. 21.
    Sevilla-Lara L, Learned-Miller E (2012) Distribution fields for tracking. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1910–1917. doi: https://doi.org/10.1109/CVPR.2012.6247891
  22. 22.
    Simon D (2006) Optimal state estimation: Kalman, H, infinity, and nonlinear approaches. John Wiley & Sons, New Jersey, pp 218–222CrossRefGoogle Scholar
  23. 23.
    Song Y, Ma C, Gong L, Zhang J, Lau HWR, Yang M (2017) CREST: convolutional residual learning for visual tracking. IEEE International Conference on Computer Vision (ICCV), Venice, Italy: 2574–2583. doi: https://doi.org/10.1109/ICCV.2017.279
  24. 24.
    Wang T. and Ling H., "Gracker: a graph-based planar object tracker," IEEE Trans Pattern Anal Mach Intell, vol. 40, no. 6, pp. 1494–1501, 1 June 2018. [DOI:  https://doi.org/10.1109/TPAMI.2017.2716350]CrossRefGoogle Scholar
  25. 25.
    Wang JT, Yang JY (2007) Object tracking based on Kalman-mean shift in occlusions. J Syst Simul 19(18):4216–4220Google Scholar
  26. 26.
    Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA: 2411–2418. doi: https://doi.org/10.1109/CVPR.2013.312
  27. 27.
    Wu T., Lu Y. and Zhu S., "Online object tracking, learning and parsing with and-or graphs," IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 12, pp. 2465–2480, 1 Dec. 2017. doi: https://doi.org/10.1109/TPAMI.2016.2644963 CrossRefGoogle Scholar
  28. 28.
    Yan JH, Chen SH, Ai SF et al (2014) Target tracking with improved CAMShift based on Kalman predictor. J Chin Inert Technol 22(4):536–542Google Scholar
  29. 29.
    Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45.  https://doi.org/10.1145/1177352.1177355 CrossRefGoogle Scholar
  30. 30.
    Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn 46(1):397–411.  https://doi.org/10.1016/j.patcog.2012.07.013 MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. Proc IEEE Conference of Computer Vision Pattern Recognition, Providence, RI, USA: 2042–2049, . doi: https://doi.org/10.1109/CVPR.2012.6247908
  32. 32.
    Zhang K, Zhang L, Yang MH (2012) Real-time compressive tracking. European Conference on Computer Vision, Florence, Italy: 864–877. doi: https://doi.org/10.1007/978-3-642-33712-3_62 CrossRefGoogle Scholar
  33. 33.
    Zhang K, Zhang L, Yang MH (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015.  https://doi.org/10.1109/TPAMI.2014.2315808 CrossRefGoogle Scholar
  34. 34.
    Zhao S, Zhang S, Zhang L (2018) Towards occlusion handling: object tracking with background estimation. IEEE Trans Cybernet 48(7):2086–2100.  https://doi.org/10.1109/TCYB.2017.2727138 CrossRefGoogle Scholar
  35. 35.
    Zhou X, Li J, Chen S, Cai H, Liu H (2018) Multiple perspective object tracking via context-aware correlation filter. IEEE Access 6:43262–43273.  https://doi.org/10.1109/ACCESS.2018.2861824 CrossRefGoogle Scholar
  36. 36.
    Zhu H, Porikli F (2017) Automatic refinement strategies for manual initialization of object trackers. IEEE Trans Image Process 26(2):821–835.  https://doi.org/10.1109/TIP.2016.2633874 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jinguang Chen
    • 1
  • Xiaoxing Li
    • 1
  • Mingming Wang
    • 1
  • Lili Ma
    • 1
  • Bugao Xu
    • 1
    • 2
    Email author
  1. 1.School of Computer ScienceXi’an Polytechnic UniversityXi’anChina
  2. 2.Department of Computer Science and EngineeringUniversity of North TexasDentonUSA

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