Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1593–1601 | Cite as

Detecting moving objects via the low-rank representation

  • Yang Zhou
  • Bingo Wing-Kuen LingEmail author
Original Paper


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.


Moving object detection Low-rank representation Concave penalty function Priori target rank information 



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).


  1. 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. 2.
    Soumya, T., Thampi, S.M.: Self-organized night video enhancement for surveillance systems. Signal Image Video Process. 11(1), 57–64 (2017)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Khan, M., Shah, T., Batool, S.I.: A new implementation of chaotic S-boxes in CAPTCHA. SIViP 10(2), 293–300 (2016)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Tao, H., Lu, X.: Contour-based smoky vehicle detection from surveillance video for alarm systems. Signal Image Video Process. 13, 217–225 (2019)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)Google Scholar
  10. 10.
    Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)MathSciNetCrossRefGoogle Scholar
  11. 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)Google Scholar
  12. 12.
    Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 1–37 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wright, J., Ganesh, A., Rao S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices, arXiv:0905.0233v2
  14. 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)Google Scholar
  15. 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. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)Google Scholar
  23. 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. 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)CrossRefGoogle Scholar
  25. 25.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Berlin (2009)zbMATHGoogle Scholar
  26. 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)Google Scholar
  27. 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)Google Scholar
  28. 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)Google Scholar
  29. 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)CrossRefGoogle Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: International Conference on Machine Learning, pp. 233–240 (2006)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Information EngineeringGuangdong University of TechnologyGuangzhouChina

Personalised recommendations