Skip to main content
SpringerLink
Log in
Book cover

International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 611–619Cite as

Incremental Robust Nonnegative Matrix Factorization for Object Tracking

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

Abstract

Nonnegative Matrix Factorization (NMF) has received considerable attention in visual tracking. However noises and outliers are not tackled well due to Frobenius norm in NMF’s objective function. To address this issue, in this paper, NMF with \(L_{2,1}\) norm loss function (robust NMF) is introduced into appearance modelling in visual tracking. Compared to standard NMF, robust NMF not only handles noises and outliers but also provides sparsity property. In our visual tracking framework, basis matrix from robust NMF is used for appearance modelling with additional \(\ell _1\) constraint on reconstruction error. The corresponding iterative algorithm is proposed to solve this problem. To strengthen its practicality in visual tracking, multiplicative update rules in incremental learning for robust NMF are proposed for model update. Experiments on the benchmark show that the proposed method achieves favorable performance compared with other state-of-the-art methods.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bucak, S.S., Gunsel, B.: Incremental subspace learning via non-negative matrix factorization. Pattern Recognit. 42(5), 788–797 (2009)

    Article  MATH  Google Scholar 

  2. Danelljan, M., Khan, F.S., Felsberg, M., Weijer, J.V.D.: Adaptive color attributes for real-time visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  3. Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: IEEE International Conference on Computer Vision, pp. 263–270 (2011)

    Google Scholar 

  4. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  5. Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1822–1829 (2012)

    Google Scholar 

  6. Kong, D., Ding, C., Huang, H.: Robust nonnegative matrix factorization using l21-norm. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 673–682. ACM (2011)

    Google Scholar 

  7. Liu, F., Zhou, T., Yang, J., Gu, I.Y.: Robust visual tracking via inverse nonnegative matrix factorization. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2016)

    Google Scholar 

  8. Ma, L., Zhang, X., Hu, W., Xing, J., Lu, J., Zhou, J.: Local subspace collaborative tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4301–4309 (2015)

    Google Scholar 

  9. Wang, D., Lu, H.: On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization. Signal Process. 93(6), 1608–1623 (2013)

    Article  Google Scholar 

  10. Wang, D., Lu, H., Yang, M.H.: Online object tracking with sparse prototypes. IEEE Trans. Image Process. 22(1), 314–325 (2013)

    Article  MathSciNet  Google Scholar 

  11. Wang, N., Wang, J., Yeung, D.Y.: Online robust non-negative dictionary learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 314–325 (2013)

    Google Scholar 

  12. Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 1–1 (2015)

    Google Scholar 

  13. Wu, Y., Shen, B., Ling, H.: Visual tracking via online nonnegative matrix factorization. IEEE Trans. Circ. Syst. Video Technol. 24(3), 374–383 (2014)

    Article  Google Scholar 

  14. Zhang, H., Zha, Z.J., Yang, Y., Yan, S., Chua, T.S.: Robust (semi) nonnegative graph embedding. IEEE Trans. Image Process. 23(7), 2996–3012 (2014)

    Article  MathSciNet  Google Scholar 

  15. Zhang, H., Hu, S., Zhang, X., Luo, L.: Visual tracking via constrained incremental non-negative matrix factorization. IEEE Signal Process. Lett. 22(9), 1350–1353 (2015)

    Article  Google Scholar 

  16. Zhang, T., Liu, S., Xu, C., Yan, S., Ghanem, B., Ahuja, N., Yang, M.H.: Structural sparse tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 150–158 (2015)

    Google Scholar 

  17. Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparse collaborative appearance model. IEEE Trans. Image Process. 23, 2356–2368 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This research is partly supported by NSFC, China (No: 61273258), 863 Plan, China (No. 2015AA042308), USCAST2015-10 and USCAST2013-07.

Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

    Fanghui Liu, Yu Qiao & Jie Yang

  2. Shanghai Institute of Spaceflight Control Technology, Shanghai, China

    Mingna Liu

  3. Infrared Detection Technology Research and Development Center, China Aerospace Science and Technology Corporation, Shanghai, China

    Mingna Liu & Tao Zhou

Authors
  1. Fanghui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingna Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Yang .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Liu, F., Liu, M., Zhou, T., Qiao, Y., Yang, J. (2016). Incremental Robust Nonnegative Matrix Factorization for Object Tracking. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_68

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46672-9_68

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation