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

Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

Abstract:

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.

Keywords: background subtraction, codebook model, dynamic background, local binary pattern, illumination changes

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110686

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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.