Advertisement

Wireless Networks

, Volume 25, Issue 7, pp 3715–3725 | Cite as

Scale adaptive correlation tracking based on convolutional features

  • Wenjing Kang
  • Xinyou Li
  • Gongliang LiuEmail author
  • Shaobo Wang
Article
  • 139 Downloads

Abstract

In recent years, several correlation tracking algorithms have been proposed exploiting hierarchical features from deep convolutional neural networks. However, most of these methods focus on utilizing the hierarchical features for target translation and use a fixed-size searching window throughout a sequence. Because of neglecting the changes of target scale, these algorithms may import error to the model and lead to drifting. Moreover, numerous factors like fast motion and heavy occlusion can induce instability of translation model which may result in the tracking failure. In this paper, we propose a novel scale adaptive tracking algorithm based on hierarchical CNN features, which learns correlation filters to locate the target and constructs a target pyramid around the estimated target position for scale estimation. In case of tracking failure, we generate an online detector of random fern classifier and activate it to re-detect the target. To evaluate the tracking algorithm, extensive experiments are conducted on a benchmark with 100 video sequences. The tracking result demonstrate that the hierarchical CNN features are well fit to handle sequences with scale variation, motion blur and illumination variation. And the online re-detection is of great importance in tracking failures caused by fast motion and heavy occlusion. The evaluation results show that our tracker outperforms the state-of-the-art methods by a huge margin.

Keywords

Correlation tracking Scale estimation CNN features Object detection 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos.61501139, 61371100, 61401118).

References

  1. 1.
    Collins, R. T. (2003). Mean-shift blob tracking through scale space. In Proceedings of the IEEE conference on computer vision and pattern recognition (vol. 2, pp. II–234).Google Scholar
  2. 2.
    Mei, X., & Ling, H. (2009). Robust visual tracking using \(l_1\) minimization. In Computer vision: 2009 IEEE 12th international conference (pp. 1436–1443).Google Scholar
  3. 3.
    He, S., Yang, Q., Lau, R. W., Wang, J., & Yang, M. H. (2103). Visual tracking via locality sensitive histograms. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2427–2434).Google Scholar
  4. 4.
    Danelljan, M., Robinson, A., Khan, F. S., & Felsberg, M. (2016). Beyond correlation filters: Learning continuous convolution operators for visual tracking. In European conference on computer vision (pp. 472–488).Google Scholar
  5. 5.
    Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O. & Torr, P. H. (2016). Staple: Complementary learners for real-time tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1401–1409).Google Scholar
  6. 6.
    Jia, X., Lu, H., & Yang, M. H. (2012). Visual tracking via adaptive structural local sparse appearance model. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1822–1829).Google Scholar
  7. 7.
    Zhang, J., Ma, S., & Sclaroff, S. (2014). Meem: Robust tracking via multiple experts using entropy minimization. In European conference on computer vision (pp. 188–203).Google Scholar
  8. 8.
    Zhang, L., Lu, H., Du, D. & Liu, L. (2016). Sparse hashing tracking. In IEEE transactions on image processing (pp. 840–849).Google Scholar
  9. 9.
    Ma, C., Huang, J. B., Yang, X. & Yang, M. H. (2015). Hierarchical convolutional features for visual tracking. In Proceedings of the IEEE international conference on computer vision (pp. 3074–3082).Google Scholar
  10. 10.
    Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J. & Yang, M. H. (2016). Hedged deep tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4303–4311).Google Scholar
  11. 11.
    Santner, J., Leistner, C., Saffari, A., Pock, T., & Bischof, H. (2010). PROST: Parallel robust online simple tracking. In: Computer vision and pattern recognition (pp. 723–730).Google Scholar
  12. 12.
    Zhong, W., Lu, H., & Yang, M. H. (2014). Robust object tracking via sparse collaborative appearance model. IEEE Transactions on Image Processing, 23, 2356–2368.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wu, Y., Lim, J., & Yang, M. H. (2015). Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1834–1848.CrossRefGoogle Scholar
  14. 14.
    Lukežič, A., Vojíř, T., Čehovin, L., Matas, J., & Kristan, M. (2017). Discriminative correlation filter with channel and spatial reliability. In Proceedings of IEEE international conference on computer vision (pp. 6309–6318).Google Scholar
  15. 15.
    Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2012). Exploiting the circulant structure of tracking-by-detection with kernels. In European conference on computer vision (pp. 702–715).Google Scholar
  16. 16.
    Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2017). ECO: Efficient convolution operators for tracking. In Computer vision and pattern recognition (pp. 1125–1141).Google Scholar
  17. 17.
    Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 583–596.CrossRefGoogle Scholar
  18. 18.
    Bolme, D. S., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010). Visual object tracking using adaptive correlation filters. In Computer vision and pattern recognition (pp. 2544–2550).Google Scholar
  19. 19.
    Danelljan, M., Häger, G., Khan, F., & Felsberg, M. (2014). Accurate scale estimation for robust visual tracking. In British machine vision conference, September 1–5, Nottingham.Google Scholar
  20. 20.
    Ma, C., Yang, X., Zhang, C., & Yang, M. H. (2015). Long-term correlation tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5388–5396).Google Scholar
  21. 21.
    Li, Y., & Zhu, J. (2014). A scale adaptive kernel correlation filter tracker with feature integration. In ECCV Workshops (pp. 254–265).Google Scholar
  22. 22.
    Kalal, Z., Mikolajczyk, K., & Matas, J. (2012). Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409–1422.CrossRefGoogle Scholar
  23. 23.
    Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).Google Scholar
  24. 24.
    Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587).Google Scholar
  25. 25.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on multimedia (pp. 675–678).Google Scholar
  26. 26.
    Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  27. 27.
    Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M. M., Hicks, S. L., et al. (2016). Struck: Structured output tracking with kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2096–2109.CrossRefGoogle Scholar
  28. 28.
    Wu, Y., Lim, J., & Yang, M. H. (2013). Online object tracking: A benchmark. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2411–2418).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wenjing Kang
    • 1
  • Xinyou Li
    • 1
  • Gongliang Liu
    • 1
    Email author
  • Shaobo Wang
    • 2
  1. 1.School of Information and Electrical EngineeringHarbin Institute of Technology (Weihai)WeihaiChina
  2. 2.Shenzhen Academy of Aerospace TechnologyShenzhenChina

Personalised recommendations