Abstract
Detecting moving objects from a video sequence based on conventional images have reached a significant level of maturity with some practical success. However, their performance may degrade under illumination changes. To solve this, the advantages of using multispectral images for background subtraction is investigated and tested over several multispectral videos using codebook algorithm in this paper. Experimental results show that multispectral images represent a viable better alternative to conventional images in the search of robust detection and motion analysis of moving objects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foregroundbackground segmentation using codebook model. Real Time Imaging 11(3), 172–185 (2005)
Xue, G., Song, L., Sun, J., Wu, M.: Hybrid center-symmetric local pattern for dynamic background subtraction. In: 2011 IEEEInternational Conference on Multimedia and Expo (ICME), p. 16. IEEE (2011)
Bouwmans, T.: Traditional and recent approaches in background modeling for foreground detection: an overview. Comput. Sci. Rev. 11, 31–66 (2014)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE ComputerSociety Conference on Computer Vision and Pattern Recognition 1999, vol. 2, pp. 246–252. IEEE (1999)
Benezeth, Y., Sidibé, D., Thomas, J.B.: Background subtraction with multispectral video sequences. In: IEEE International Conference on Robotics and Automation workshop on Non-classical Cameras, Camera Networks and Omnidirectional Vision(OMNIVIS), 6 p. (2014)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151–1163 (2002)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: IEEE International Conference on Image Processing, vol. 5, pp. 3061–3064. IEEE (2004)
Kim, K., Harwood, D., Davis, L.S.: Background updating for visual surveillance. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds.) ISVC 2005. LNCS, vol. 3804, pp. 337–346. Springer, Heidelberg (2005). https://doi.org/10.1007/11595755_41
Murgia, J., Meurie, C., Ruichek, Y.: All-day moving objects detection for security at level crossing. In: TRA2014-Transport Research Arena: Transport Solutions: from Research to Deployment-Innovate Mobility, Mobilise Innovation! 10 p. (2014)
Bouchech, H.: Selection of optimal narrowband multispectral images for face recognition. Ph.D. thesis, Dijon (2015)
Viau, C., Payeur, P., Cretu, A.M.: Multispectral image analysis for object recognition and classification. In: SPIE Defense+Security, p. 98440N. International Society for Optics and Photonics (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, R., Ruichek, Y., El Bagdouri, M. (2017). Background Subtraction with Multispectral Images Using Codebook Algorithm. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-70353-4_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70352-7
Online ISBN: 978-3-319-70353-4
eBook Packages: Computer ScienceComputer Science (R0)