Advertisement

Multi-target Tracking Algorithm Based on Convolutional Neural Network and Guided Sample

  • You Zhou
  • Yujuan Ma
  • Guijin HanEmail author
  • Linli Sun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

In order to reduce the number of samples when using the convolutional neural network to train the moving target template online and improve the validity of samples, a sample selection method based on guided samples is proposed and applied to the fast multi-domain convolutional neural network tracking algorithm. The basic idea of the sample selection method is as follows, the initial samples are determined firstly by the sample filtering method of frame level detection and nonlinear regression model, and then the similarity between the initial samples and the target template are calculated, the samples with the similarity greater than a certain threshold are finally used as the guidance sample. The experimental results show that the tracking time of the proposed tracking algorithm is greatly reduced compared with the fast multi-domain convolutional neural network, the proposed tracking algorithm can speed up the tracking speed, improve the accuracy and robustness in complex environments.

Keywords

Target tracking Convolutional neural network Similarity measure 

Notes

Acknowledgments

This work was supported by the Department of Education Shaanxi Province, China, under Grant 2013JK1023.

References

  1. 1.
    Li, N., Li, D.X., Liu, W.H., Liu, Y.: Object tracking algorithm with multiple instance learning. J. Xi’an Univ. Posts Telecommun. 19, 43–47 (2014).  https://doi.org/10.13682/j.issn.2095-6533.2014.02.007CrossRefGoogle Scholar
  2. 2.
    Li, K., Liu, Y., Li, N., Wang, W.J.: Scale adaptive object tracking based on multiple features integration. J. Xi’an Univ. Posts Telecommun. 21, 44–50 (2016).  https://doi.org/10.13682/j.issn.2095-6533.2016.06.009CrossRefGoogle Scholar
  3. 3.
    Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012).  https://doi.org/10.1109/TPAMI.2011.239CrossRefGoogle Scholar
  4. 4.
    Henriques, J.F., Rui, C., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 583–596 (2014).  https://doi.org/10.1109/tpami.2014.2345390CrossRefGoogle Scholar
  5. 5.
    Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: IEEE International Conference on Computer Vision, pp. 3119–3127. IEEE Press, Santiago (2016).  https://doi.org/10.1109/ICCV.2015.357
  6. 6.
    Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks, pp. 749–765 (2016).  https://doi.org/10.1007/978-3-319-46448-0_45CrossRefGoogle Scholar
  7. 7.
    Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Computer Vision and Pattern Recognition, pp. 4293–4302. IEEE Press, Las Vegas (2016).  https://doi.org/10.1109/cvpr.2016.465
  8. 8.
    Qin, Y., He, S., Zhao, Y., Gong, Y.: RoI pooling based fast multi-domain convolutional neural networks for visual tracking. In: International Conference on Artificial Intelligence and Industrial Engineering, (2016).  https://doi.org/10.2991/aiie-16.2016.46
  9. 9.
    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. 1442–68 (2013).  https://doi.org/10.1109/tpami.2013.230
  10. 10.
    Kristan, M., Leonardis, A., Matas, J.: The visual object tracking VOT 2016 challenge results. In: IEEE International Conference on Computer Vision Workshops, pp. 98–111. IEEE Press (2013).  https://doi.org/10.1007/978-3-319-48881-3_54Google Scholar
  11. 11.
    Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: IEEE International Conference on Computer Vision Workshop, pp. 621–629. IEEE Press, Santiago (2016).  https://doi.org/10.1109/iccvw.2015.84
  12. 12.
    Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: MUlti-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: Computer Vision and Pattern Recognition, pp. 749–758. IEEE Press, Boston (2015).  https://doi.org/10.1109/cvpr.2015.7298675
  13. 13.
    Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization, pp. 188–203. Springer (2014).  https://doi.org/10.1007/978-3-319-10599-4_13Google Scholar
  14. 14.
    Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Lecture Notes in Computer Science, pp. 254–265 (2014).  https://doi.org/10.1007/978-3-319-16181-5_18Google Scholar
  15. 15.
    Danelljan, M., Hager, 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. IEEE Press, Santiago (2016).  https://doi.org/10.1109/iccv.2015.490

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an University of Post and TelecommunicationsXi’anChina

Personalised recommendations