Object Tracking Based on Mean Shift Algorithm and Kernelized Correlation Filter Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)


In order to solve the problems of motion blur and fast motion, a new robust object tracking algorithm using the Kernelized Correlation Filters (KCF) and the Mean Shift (MS) algorithm, called KCFMS is presented in this paper. The object tracking process can be described as: First, we give the initial position and size of the object and use the Mean Shift algorithm to obtain the position of the object. Second, the Kernelized Correlation Filtering algorithm is used to obtain the position of the object in the same frame. Third, we use the cross update strategy to update the object models. In order to improve the tracking speed as much as possible, our object tracking algorithm works only over one layer. This hybrid algorithm has a good tracking effect on the target fast motion and motion blur. We present extensive experimental results on a number of challenging sequences in terms of efficiency, accuracy and robustness.


Kernelized Correlation Filters Mean shift Motion blur Fast motion 



This work is partially supported by the National Natural Science Foundation of China (61402310). Natural Science Foundation of Jiangsu Province of China (BK20141195).


  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 1–17 (2006)CrossRefGoogle Scholar
  2. 2.
    Lasserre, J.A., Bishop, C.M., Minka, T.P.: Principled hybrids of generative and discriminative models. In: 19th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 87–94. IEEE Computer Society, New York (2006)Google Scholar
  3. 3.
    Ng, A., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and Naive Bayes. In: Proceedings of Advances in Neural Information Processing, vol. 28, no. 3, pp. 169–187 (2001)Google Scholar
  4. 4.
    Lin, R.S., Ross, D.A., Lim, J., et al.: Adaptive discriminative generative model and its applications. In: Neural Information Processing Systems, pp. 801–808 (2004)Google Scholar
  5. 5.
    Yang, M., Wu, Y.: Tracking non-stationary appearances and dynamic feature selection. In: 18th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1059–1066. IEEE Computer Society, San Diego (2005)Google Scholar
  6. 6.
    Yu, Q., Dinh, T.B., Medioni, G.: Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 678–691. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88688-4_50 CrossRefGoogle Scholar
  7. 7.
    Tang, F., Brennan, S., Zhao, Q., et al.: Co-tracking using semi-supervised support vector machines. In: 9th IEEE International Conference on Computer Vision, pp. 1–8. IEEE (2003)Google Scholar
  8. 8.
    Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: 13th International Conference on Neural Information Processing Systems, vol. 1, pp. 388–394. MIT Press, Denver (2000)Google Scholar
  9. 9.
    Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)CrossRefGoogle Scholar
  10. 10.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: bootstrapping binary classifiers by structural constraints. In: 23rd IEEE Conference on Computer Vision and Pattern Recognition, vol. 238, pp. 49–56. IEEE Computer Society, San Francisco (2010)Google Scholar
  11. 11.
    Comaniciu, D., Menber, V.R., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)CrossRefGoogle Scholar
  12. 12.
    Henriques, J.F., Rui, C., Martins, P., et al.: High-speed tracking with Kernelized Correlation Filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)CrossRefGoogle Scholar
  13. 13.
    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. IEEE Trans. Comput. Vis. Pattern Recogn. 37(9), 1834–1848 (2015)CrossRefGoogle Scholar
  14. 14.
    Zhang, K., Zhang, L., Yang, M.H.: Fast compressive tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2014)CrossRefGoogle Scholar
  15. 15.
    Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 127–141. Springer, Cham (2014). doi: 10.1007/978-3-319-10602-1_9 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

Personalised recommendations