Abstract
Robust Principal Component Analysis (RPCA) has been proved to be effective for the moving object detection with background variation. Alternating Direction Method (ADM) based RPCA takes full advantages of the separable structure of the objective function to achieve better results than traditional RPCA methods. But it suffers from the heavy computing burden and low efficiency. In this paper, a Symmetric Alternating Direction Method (SADM) is proposed to solve above problems. SADM optimizes the iterative strategy of ADM by updating the multiplier of the linear constraint twice every iteration which speeds up the convergence, thus reduces the execution times of Singular Value Decomposition (SVD). Besides, the new equilibrium parameter and interrupt mechanism are introduced to guarantee the object detection accuracy and avoid the unnecessary iterations. Compared with ADM, the experimental results show that not only the detection accuracy of proposed method is improved by 46.8%, but also the time consumption is reduced by 97.5%.
This work was supported by the Project of Beijing Municipal Commission of Education (KM201710028017), the National Natural Science Foundation of China (61572331, 61472468, 61373034), the National Key Technology Research and Development Program (2015BAF13B01), the International Cooperation Program on Science and Technology (2011DFG13000), the Project of Beijing Municipal Science & Technology Commission (Z141100002014001), the Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (IDHT20150507), and Training young backbone talents personal projects (2014000020124G135).
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References
Zeng, W., Huang, Q.: Moving object segmentation: a block-based moving region detection approach. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 280–287. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30541-5_35
Gao, S., Han, Z., Li, C., Jiao, J.: Real-time multi-pedestrian tracking based on vision and depth information fusion. In: Huet, B., Ngo, C.-W., Tang, J., Zhou, Z.-H., Hauptmann, A.G., Yan, S. (eds.) PCM 2013. LNCS, vol. 8294, pp. 708–719. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03731-8_66
Mu, K., Hui, F., Zhao, X.: Multiple vehicle detection and tracking in highway traffic surveillance video based on sift feature matching. J. Inf. Process. Syst. 12(2), 183–195 (2016)
Zhang, X., Gao, H., Mu, G., Li, G., Liu, Y., Li, D.: A study on key technologies of unmanned driving. CAAI Trans. Intell. Technol. 1(1), 4–13 (2016)
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 (2017)
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)
Yuan, W., Liang, X., Chen, H., Na, L., Zou, T.: A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem. J. Comput. Theor. Nanosci. 13(6), 3722–3733 (2016)
Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)
Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Advances in Neural Information Processing Systems, pp. 2080–2088 (2009)
Lin, Z., Ganesh, A., Wright, J., Wu, L., Chen, M., Ma, Y.: Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. In: Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), vol. 61, no. 6 (2009)
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: Advances in Neural Information Processing Systems, pp. 612–620 (2011)
Guyon, C., Bouwmans, T., Zahzah, E.-H.: Moving object detection by robust PCA solved via a linearized symmetric alternating direction method. In: Bebis, G., et al. (eds.) ISVC 2012. LNCS, vol. 7431, pp. 427–436. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33179-4_41
Yuan, X., Yang, J.: Sparse and low-rank matrix decomposition via alternating direction methods, p. 12 (2009). Preprint
He, H., Zhang, M., Han, D., Chen, Y.: A new alternating direction method for solving separable variational inequality problems. Scientia Sinica Mathematica 42(2), 133–149 (2012)
Glowinski, R., Tallec, P.L.: Augmented Lagrangian and operator-splitting methods in nonlinear mechanics. In: SIAM (1989)
Rodriguez, P., Wohlberg, B.: Fast principal component pursuit via alternating minimization. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 69–73. IEEE (2013)
Zhao, Q., Meng, D., Xu, Z., Zuo, W., Zhang, L.: Robust principal component analysis with complex noise. In: ICML, pp. 55–63 (2014)
Hintermüller, M., Tao, W.: Robust principal component pursuit via inexact alternating minimization on matrix manifolds. J. Math. Imaging Vis. 51(3), 361–377 (2015)
Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 255–261 (1999)
Maddalena, L., Petrosino, A.: A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Comput. Appl. 19(2), 179–186 (2010)
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Shao, Z., Wu, G., Qu, Y., Shi, Z., Guan, Y., Tan, J. (2018). Robust Principal Component Analysis via Symmetric Alternating Direction for Moving Object Detection. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_27
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