Skip to main content
SpringerLink
Log in

Detecting moving objects via the low-rank representation

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

Abstract

Moving object detection is a fundamental and necessary step in many computer vision algorithms. These algorithms are built in many intelligent devices such as in the smartphones, the tachographs and the personal video recorders. Recently, the methods for performing the moving object detection based on the low-rank representation have been proposed. For these methods, it is assumed that the background is represented by a low-rank matrix. On the other hand, the foreground objects cannot be represented by low-rank matrices. They are seen as the outliers. Hence, detecting the contiguous outliers in the low-rank representation (DECELOR) can be formulated as an extension of the robust principal component analysis problem. This method fully utilizes the spatial continuity of the foreground regions. To achieve a more accurate detection, this paper integrates both the concave penalty function and the priori target rank information into a single optimization problem based on the DECELOR formulation. The optimization problem is efficiently solved by an alternating direction scheme. The computer numerical simulation results on the real-world scenes demonstrate the superiority of our method in terms of the effective handling of a wide range of complex scenarios.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Mehdi, S., Hoda, R., Alireza, A., Mahoud, R.H., Shervin, S.: A receiver aware H.264/AVC encoder for decoder complexity control in mobile applications. Signal Image Video Process. 11(3), 431–438 (2016)

    Google Scholar 

  2. Soumya, T., Thampi, S.M.: Self-organized night video enhancement for surveillance systems. Signal Image Video Process. 11(1), 57–64 (2017)

    Article  Google Scholar 

  3. Shi, Y., Wang, X.P., Fan, H.F.: Light-weight white-box encryption scheme with random padding for wearable consumer electronic devices. IEEE Trans. Consum. Electron. 63(1), 44–52 (2017)

    Article  Google Scholar 

  4. Raheja, J.L., Chaudhary, A., Nandhini, K., Maiti, S.: Pre-consultation help necessity detection based on gait recognition. SIViP 9(6), 1357–1363 (2015)

    Article  Google Scholar 

  5. Khan, M., Shah, T., Batool, S.I.: A new implementation of chaotic S-boxes in CAPTCHA. SIViP 10(2), 293–300 (2016)

    Article  Google Scholar 

  6. Artur, J., Leonardo, A.B.T., William, R.S.: Novel approaches to human activity recognition based on accelerometer data. SIViP 12(7), 1387–1394 (2018)

    Article  Google Scholar 

  7. Tao, H., Lu, X.: Contour-based smoky vehicle detection from surveillance video for alarm systems. Signal Image Video Process. 13, 217–225 (2019)

    Article  Google Scholar 

  8. Hadiuzzaman, M., Haque, N., Rahman, F., Hossain, S., Siam, M.R.K., Qiu, T.Z.: Pixel-based heterogeneous traffic measurement considering shadow and illumination variation. SIViP 11(7), 1245–1252 (2017)

    Article  Google Scholar 

  9. Shimada, A., Arita, D., Taniguchi, R.I.: Dynamic control of adaptive mixture-of-Gaussians background model. In: IEEE International Conference on Video and Signal Based Surveillance, pp. 5 (2006)

  10. Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)

    Article  MathSciNet  Google Scholar 

  11. Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the Pixel-based adaptive segmenter. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–43 (2012)

  12. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 1–37 (2009)

    Article  MathSciNet  Google Scholar 

  13. Wright, J., Ganesh, A., Rao S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices, arXiv:0905.0233v2

  14. Wagner, A., Wright, J., Ganesh, A., Zhou, Z.H., Ma, Y.: Towards a practical face recognition system: robust registration and illumination by sparse representation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 597–604 (2009)

  15. Cai, N., Zhou, Y., Ye, Q., Liu, G., Wan, H., Chen, X.D.: A new IC solder joint inspection via robust principal component analysis. IEEE Trans. Compon. Packag. Manuf. Technol. 7(2), 300–309 (2017)

    Google Scholar 

  16. 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. Pattern Anal. Mach. Intell. 34(11), 2233–2246 (2012)

    Article  Google Scholar 

  17. Yao, M.H., Jie, L.I., Wang, X.B.: Solar cells surface defects detection of using RPCA method. Chin. J. Comput. 36(9), 1943–1952 (2013)

    Article  Google Scholar 

  18. Bouwmans, T., Zahzah, E.H.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. 122, 22–34 (2014)

    Article  Google Scholar 

  19. Bouwmans, T., Sobral, A., Javed, S., Jung, S.K., Zahzah, E.H.: Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset. Comput. Sci. Rev. 23, 1–71 (2016)

    Article  Google Scholar 

  20. Gao, B., Lu, P., Woo, W.L., Tian, G.Y.: Variational Bayes sub-group adaptive sparse component extraction for diagnostic imaging system. IEEE Trans. Ind. Electron. 65(10), 8142–8152 (2018)

    Article  Google Scholar 

  21. Lu, P., Gao, B., Woo, W.L., Li, X., Tian, G.Y.: Automatic relevance determination of adaptive variational Bayes sparse decomposition for micro-cracks detection in thermal sensing. IEEE Sens. J. 17(16), 5220–5230 (2017)

    Article  Google Scholar 

  22. Zhou, Q., Meng, D.Y., Xu, Z., Zuo, W., Zhang, L.: Robust principal component analysis with complex noise. In: International Conference on Machine Learning, pp. 55–63 (2014)

  23. Gan, C., Wang, Y., Wang, X.: Multi-feature robust principal component analysis for video moving object segmentation. J. Image Gr. 18(9), 1124–1132 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  25. Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Berlin (2009)

    MATH  Google Scholar 

  26. Lu, C.Y., Tang, J.H., Ya, S.C., Lin, Z.C.: Generalized nonconvex nonsmooth low-rank minimization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4130–4137 (2014)

  27. Oh, T.H., Kim, H., Tai, Y.W., Bazin, J.C., Kweon, I.S.: Partial sum minimization of singular value in RPCA for low-level vision. In: IEEE International Conference on Computer Vision, pp. 145–152 (2013)

  28. Lin, Z., Chen, M., Wu, L., Ma, Y.: The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. In: UIUC Technical Report (2009)

  29. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  30. Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)

    Article  Google Scholar 

  31. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: International Conference on Machine Learning, pp. 233–240 (2006)

Download references

Acknowledgements

This paper is supported partly by the National Nature Science Foundation of China (Nos. U1701266, 61372173, 61471132 and 61671163), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (No. 501130144), the Natural Science Foundation of Guangdong Province China (No. 2014A030310346) and the Science and Technology Planning Project of Guangdong Province China (No. 2015A030401090).

Author information

Authors and Affiliations

  1. Faculty of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Yang Zhou & Bingo Wing-Kuen Ling

Authors
  1. Yang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bingo Wing-Kuen Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bingo Wing-Kuen Ling.

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

Zhou, Y., Ling, B.WK. Detecting moving objects via the low-rank representation. SIViP 13, 1593–1601 (2019). https://doi.org/10.1007/s11760-019-01503-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01503-7

Keywords

Search

Navigation