Skip to main content
SpringerLink
Log in

Local null space pursuit for real-time moving object detection in aerial surveillance

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Recently, accurate detection of moving objects has achieved via principal component pursuit (PCP). However, in the case of aerial imagery, existing PCP-based detection methods suffer from low accuracy and/or high computational loads. This paper presents a novel S-PCP method, called local null space pursuit (LNSP), which achieves a high detection accuracy and real-time performance on aerial images. LNSP models the background as a subspace that lies in a low-dimensional subspace, while the moving objects are modelled as sparse. Based on these two models, LNSP proposes a new formulation for the detection problem by using multiple local null spaces and \(\ell _1\)-norm. The performance of LNSP is evaluated on challenging aerial datasets and then compared the results with relevant current state-of-the-art methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Balzano, L., Nowak, R., Recht, B.: Online identification and tracking of subspaces from highly incomplete information. In: 2010 48th Annual Allerton Conference on Communication, Control, and Computing, pp. 704–711. IEEE (2010)

  2. Bertsekas, D.P.: Convex Optimization Theory. Athena Scientific, Belmont (2009)

    MATH  Google Scholar 

  3. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? JACM 58(3), 11 (2011)

    Article  MathSciNet  Google Scholar 

  4. Chaquet, J.M., Carmona, E.J., Fernández-Caballero, A.: A survey of video datasets for human action and activity recognition. CVIU 117(6), 633–659 (2013)

    Google Scholar 

  5. Chau, G., Rodrıguez, P.: Panning and jitter invariant incremental principal component pursuit for video background modeling. In: IEEE Conference on CVPR, pp. 1844–1852. IEEE (2017)

  6. Collins, R., Zhou, X., Teh, S.K.: An open source tracking testbed and evaluation web site. In: IEEE Inter Workshop on Performance Evaluation of Tracking and Surveillance, pp. 17–24 (2005)

  7. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M., et al.: Eco: Efficient convolution operators for tracking. In: CVPR, vol. 1, p. 3 (2017)

  8. Dembo, R.S., Eisenstat, S.C., Steihaug, T.: Inexact newton methods. SIAM J. Numer. Anal. 19(2), 400–408 (1982)

    Article  MathSciNet  Google Scholar 

  9. Eisenstat, S.C., Walker, H.F.: Choosing the forcing terms in an inexact newton method. SIAM J. Sci. Comput. 17(1), 16–32 (1996)

    Article  MathSciNet  Google Scholar 

  10. ElTantawy, A., Shehata, M.S.: Moving object detection from moving platforms using Lagrange multiplier. In: 2015 IEEE Inter Conf on ICIP, pp. 2586–2590. IEEE (2015)

  11. ElTantawy, A., Shehata, M.S.: Ut-maro: Unscented transformation and matrix rank optimization for moving objects detection in aerial imagery. In: Inter Symposium on Visual Computing, pp. 275–284. Springer (2015)

  12. ElTantawy, A., Shehata, M.S.: A novel method for segmenting moving objects in aerial imagery using matrix recovery and physical spring model. In: 2016 23rd Inter Conference on ICPR, pp. 3898–3903. IEEE (2016)

  13. ElTantawy, A., Shehata, M.S.: MARO: matrix rank optimization for the detection of small-size moving objects from aerial camera platforms. Signal Image Video Process. 12(4), 641–649 (2017)

    Article  Google Scholar 

  14. ElTantawy, A., Shehata, M.S.: KRMARO: aerial detection of small-size ground moving objects using kinematic regularization and matrix rank optimization. IEEE Trans. Circuit Syst. Video Technol. 29(6), 1672–1686 (2018). https://doi.org/10.1109/TCSVT.2018.2843761

    Article  Google Scholar 

  15. Feng, J., Xu, H., Yan, S.: Online robust pca via stochastic optimization. In: Advances in Neural Information Processing Systems, pp. 404–412 (2013)

  16. Guo, H., Qiu, C., Vaswani, N.: An online algorithm for separating sparse and low-dimensional signal sequences from their sum. IEEE Trans. Signal Process. 62(16), 4284–4297 (2014)

    Article  MathSciNet  Google Scholar 

  17. Hastie, T., Tibshirani, R., Wainwright, M.: Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press, Boca Raton (2015)

    Book  Google Scholar 

  18. He, J., Balzano, L., Szlam, A.: Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video. In: 2012 IEEE Conference on CVPR, pp. 1568–1575. IEEE (2012)

  19. Hoffman, K., Kunze, R.: Linear Algebra. Pearson, Upper Saddle River (1971)

    MATH  Google Scholar 

  20. Mansour, H.: A short note on improved roseta. arXiv preprint arXiv:1710.05961 (2017)

  21. Mansour, H., Jiang, X.: A robust online subspace estimation and tracking algorithm. In: 2015 IEEE International Conference on ICASSP, pp. 4065–4069. IEEE (2015)

  22. Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J., Lee, H., Davis, L., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: 2011 IEEE Conference on CVPR, pp. 3153–3160. IEEE (2011)

  23. Oreifej, O., Li, X., Shah, M.: Simultaneous video stabilization and moving object detection in turbulence. IEEE Trans. PAMI 35(2), 450–462 (2013)

    Article  Google Scholar 

  24. Peng, Y., Ganesh, A., Wright, J., Xu, W., Ma, Y.: Rasl: Robust alignment by sparse and low-rank decomposition for linearly correlated images. In: 2010 IEEE International Conference on CVPR, pp. 763–770. IEEE (2010)

  25. Peng, Y., Ganesh, A., Wright, J., Xu, W., Ma, Y.: Rasl: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE Trans. PAMI 34(11), 2233–2246 (2012)

    Article  Google Scholar 

  26. Qiu, C., Vaswani, N.: Reprocs: A missing link between recursive robust PCA and recursive sparse recovery in large but correlated noise. arXiv preprint arXiv:1106.3286 (2011)

  27. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  28. Rodriguez, P., Wohlberg, B.: Fast principal component pursuit via alternating minimization. In: 2013 20th IEEE International Conference on ICIP, pp. 69–73. IEEE (2013)

  29. Rodriguez, P., Wohlberg, B.: Incremental principal component pursuit for video background modeling. J. Math. Imaging Vis. 55(1), 1–18 (2016)

    Article  MathSciNet  Google Scholar 

  30. Rodrıguez, P., Wohlberg, B.: An incremental principal component pursuit algorithm via projections onto the l1 ball. In: XXIV Inter Congress of Electrical Engineering, Electronics and Computing (2017)

  31. Shakeri, M., Zhang, H.: Corola: A sequential solution to moving object detection using low-rank approximation. arXiv preprint arXiv:1505.03566 (2015)

  32. Shakeri, M., Zhang, H.: Corola: a sequential solution to moving object detection using low-rank approximation. Comput. Vis. Image Underst. 146, 27–39 (2016)

    Article  Google Scholar 

  33. Veon, K.L., Mahoor, M.H., Voyles, R.M.: Video stabilization using sift-me features and fuzzy clustering. In: 2011 IEEE/RSJ International Conference on IROS, pp. 2377–2382. IEEE (2011)

  34. Xu, J., Ithapu, V.K., Mukherjee, L., Rehg, J.M., Singh, V.: Gosus: Grassmannian online subspace updates with structured-sparsity. In: 2013 IEEE International Conference on ICCV, pp. 3376–3383. IEEE (2013)

  35. Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. PAMI 35(3), 597–610 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Agwad ElTantawy & Mohamed S. Shehata

Authors
  1. Agwad ElTantawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed S. Shehata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Agwad ElTantawy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

ElTantawy, A., Shehata, M.S. Local null space pursuit for real-time moving object detection in aerial surveillance. SIViP 14, 87–95 (2020). https://doi.org/10.1007/s11760-019-01528-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01528-y

Keywords

Search

Navigation