Multi-Layer Multi-Feature Background Subtraction Using Codebook Model Framework

Background modeling and subtraction in video analysis has been widely used as an effective method for moving objects detection in many computer vision applications. Recently, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are the most frequently occurred problems in the practical situation. This paper presents a favorable two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean value of each RGB color channel. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the output of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate very competitive performance compared to previous models.

Authors:

• Yun-Tao Zhang
• Jong-Yeop Bae
• Whoi-Yul Kim

Keywords:

• Background subtraction
• codebook model
• local binary pattern
• dynamic background
• illumination changes.

References:

[1] K. Kim, H. C. Thanarat, H. David, and L. Davis. "Real-time foreground–
background segmentation using codebook model." Real-time imaging 11,
no. 3 (2005): 172-185. 15–64.
[2] M. Shah, J. D. Deng, and B. J. Woodford. "A Self-adaptive CodeBook
(SACB) model for real-time background subtraction." Image and Vision
Computing 38 (2015): 52-64.
[3] S. Brutzer, B. Höferlin, and G. Heidemann. "Evaluation of background
subtraction techniques for video surveillance." In Computer Vision and
Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 1937-1944.
IEEE, 2011.
[4] T. Bouwmans, F. El Baf, and B. Vachon. "Background modeling using
mixture of gaussians for foreground detection-a survey." Recent Patents
on Computer Science 1, no. 3 (2008): 219-237.
[5] C. Stauffer, and W. E. L. Grimson. "Adaptive background mixture
models for real-time tracking." In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., vol. 2. IEEE,
1999.
[6] M. Heikkilä, M. Pietikäinen, and J. Heikkilä. "A texture-based method for
detecting moving objects." In BMVC, pp. 1-10. 2004.
[7] O. Barnich, and M. Van Droogenbroeck. "ViBe: a powerful random
technique to estimate the background in video sequences." In Acoustics,
Speech and Signal Processing, 2009. ICASSP 2009. IEEE International
Conference on, pp. 945-948. IEEE, 2009.
[8] T. Ojala, M. Pietikäinen, and T. Mäenpää. "Multiresolution gray-scale
and rotation invariant texture classification with local binary
patterns." Pattern Analysis and Machine Intelligence, IEEE Transactions
on 24, no. 7 (2002): 971-987.
[9] J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu. "Hierarchical
method for foreground detection using codebook model." Circuits and
Systems for Video Technology, IEEE Transactions on 21, no. 6 (2011):
804-815.
[10] N. Goyette, P. M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar.
"Changedetection. net: A new change detection benchmark dataset."
InComputer Vision and Pattern Recognition Workshops (CVPRW), 2012
IEEE Computer Society Conference on, pp. 1-8. IEEE, 2012.
[11] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. "Wallflower:
Principles and practice of background maintenance." In Computer
Vision, 1999. The Proceedings of the Seventh IEEE International
Conference on, vol. 1, pp. 255-261. IEEE, 1999.
[12] A. Sobral. "BGSLibrary: An opencv c++ background subtraction library."
In IX Workshop de Visao Computacional (WVC’2013), Rio de Janeiro,
Brazil. 2013.