Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2015 | OriginalPaper | Chapter

A Sparse Error Compensation Based Incremental Principal Component Analysis Method for Foreground Detection

Authors : Ming Qin, Yao Lu, Huijun Di, Tianfei Zhou

Published in: Advances in Multimedia Information Processing -- PCM 2015

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Foreground detection is a fundamental task in video processing. Recently, many background subspace estimation based foreground detection methods have been proposed. In this paper, a sparse error compensation based incremental principal component analysis method, which robustly updates background subspace and estimates foreground, is proposed for foreground detection. There are mainly two notable features in our method. First, a sparse error compensation process via a probability sampling procedure is designed for subspace updating, which reduces the interference of undesirable foreground signal. Second, the proposed foreground detection method could operate without an initial background subspace estimation, which enlarges the application scope of our method. Extensive experiments on multiple real video sequences show the superiority of our method.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11, 31–66 (2014)CrossRefMATH
2.
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)CrossRefMATH
3.
Brand, M.: Incremental singular value decomposition of uncertain data with missing values. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 707–720. Springer, Heidelberg (2002) CrossRef
4.
5.
Guo, X., Cao, X.: Speeding up low rank matrix recovery for foreground separation in surveillance videos. In: IEEE International Conference on Multimedia and Expo (ICME 2014), pp. 1–6 July 2014
6.
Hall, P., Marshall, D., Martin, R.: Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image Vis. Comput. 20(13), 1009–1016 (2002)CrossRef
7.
He, J., Balzano, L., Szlam, A.: Incremental gradient on the grassmannian for online foreground and background separation in subsampled video. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012) pp. 1568–1575. IEEE (2012)
8.
Levey, A., Lindenbaum, M.: Sequential karhunen-loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)CrossRefMATH
9.
Li, L., Huang, W., Gu, I.H., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans. Image Process. 13(11), 1459–1472 (2004)CrossRef
10.
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint. arXiv:​1009.​5055 (2010)
11.
Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. Adv. Neural Inf. Process. Sys. 24, 612–620 (2011). Curran Associates, Inc
12.
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. Comput. Adv. Multi Sens. Adapt. Process. (CAMSAP) 61, 199 (2009)
13.
Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. In: International Conference on Vision Systems (1999)
14.
Qiu, C., Vaswani, N.: Real-time robust principal components pursuit. In: 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 591–598. IEEE (2010)
15.
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)
16.
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1–3), 125–141 (2008)CrossRef
17.
Wang, L., Wang, L., Wen, M., Zhuo, Q., Wang, W.: Background subtraction using incremental subspace learning. In: IEEE International Conference on Image Processing (ICIP 2007), vol. 5, pp. V - 45–V - 48, September 2007
18.
Xue, G., Song, L., Sun, J.: Foreground estimation based on linear regression model with fused sparsity on outliers. IEEE Trans. Circuits Syst. Video Techn. 23(8), 1346–1357 (2013)CrossRef
19.
Xue, G., Song, L., Sun, J., Zhou, J.: Foreground detection: Combining background subspace learning with object smoothing model. In: IEEE International Conference on Multimedia and Expo (ICME 2013), pp. 1–6. IEEE (2013)
20.
Zhou, T., Tao, D.: Godec: Randomized low-rank and sparse matrix decomposition in noisy case. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 33–40 (2011)
Metadata
Title
A Sparse Error Compensation Based Incremental Principal Component Analysis Method for Foreground Detection
Authors
Ming Qin
Yao Lu
Huijun Di
Tianfei Zhou
Copyright Year
2015
Book
Advances in Multimedia Information Processing -- PCM 2015

Print ISBN: 978-3-319-24074-9

Electronic ISBN: 978-3-319-24075-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-24075-6_23