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http://dx.doi.org/10.5909/JBE.2015.20.5.718

Video Segmentation Using DCT and Guided Filter in real time  

Shin, Hyunhak (Kyung Hee University)
Lee, Zucheul (kt fusion technology institute)
Kim, Wonha (Kyung Hee University)
Publication Information
Journal of Broadcast Engineering / v.20, no.5, 2015 , pp. 718-727 More about this Journal
Abstract
In this paper, we present a novel segmentation method that can extract new foreground objects from a current frame in real-time. It is performed by detecting differences between the current frame and reference frame taken from a fixed camera. We minimize computing complexity for real-time video processing. First DCT (Discrete Cosine Transform) is utilized to generate rough binary segmentation maps where foreground and background regions are separated. DCT shows better result of texture analysis than previous methods where texture analysis is performed in spatial domain. It is because texture analysis in frequency domain is easier than that in special domain and intensity and texture in DCT are taken into account at the same time. We maximize run-time efficiency of DCT by considering color information to analyze object region prior to DCT process. Last we use Guided filter for natural matting of the generated binary segmentation map. In general, Guided filter can enhance quality of intermediate result by incorporating guidance information. However, it shows some limitations in homogeneous area. Therefore, we present an additional method which can overcome them.
Keywords
DCT; matting; segmentation; Guided Filter;
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1 T. porter and T.Duff. "Compositing Digital Images," Computer Graphics Volume 18, Number 3 July 1984.   DOI
2 M.Heikkila and M.Pietikainen, "A Texture-Based Method for Modeling the Background and Detecting Moving Objects," IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 28, NO. 4, April 2006.   DOI
3 L. Li, W. Huang, I. Y.H. Gu, and Q. Tian, "Foreground Object Detection from Videos Containing Complex Background," In Proceedings of the Eleventh ACM International Conference on Multimedia, Berkeley, CA, USA, 2-8 November 2003.
4 L. Jeisung and P. Mignon, "An Adaptive Background Subtraction Method Based on Kernel Density Estimation," Sensors journal, 2012.
5 R. Rodriguez-Gomez, E. J. Fernandez-Sanchez, J. Diaz and E. Ros, "FPGA Implementation for Real-Time Background Subtraction Based on Horprasert Model," Sensors journal, 2012.
6 V.Ganapathi, C. Plagemann, D. Koller and S. Thrun, "Real Time Motion Capture Using a Single Time-Of-Flight Camera," In Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13-18 pp. 755-762. June 2010.
7 V. Kolmogorov, A. Criminisi, A. Blake, G. Cross and C. Rother, "Bi-layer segmentation of binocular stereo video," In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 20-25 pp. 407-414. June 2005.
8 E. J. Fernandez-Sanchez and J.Diaz and E.Ros, "Background Subtraction Based on Color and Depth Using Active Sensors," Sensors journal, 2013.
9 H.Kaiming, J. Sun and X. Tang, "Guided Image Filtering," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 6, June 2013.
10 H. H. Y. Tong and A. N. Venetsanopoulos, "A Perceptual Model for JPEG Applications based on Block Classification, Texture Masking, and Luminance Masking," Proceedings of IEEE Internatiopnal Conference Image Processing (ICIP) ,1998.
11 J.J. Verbeek, N. Vlassis and B. Kr ose, "Efficient Greedy Learning of Gaussian Mixture Models," Published in Neural Computation 15(2), pages 469-485, 2003.   DOI
12 A. Shimada and R.I Taniguchi, "Hybrid Background Model using Spatial-Temporal LBP," DOI: 10.1109/AVSS.2009.12 Conference: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, 2-4 September 2009.
13 T. Yu, C. Zhang, M. Cohen, Y. Rui and Y. Wu, "Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models," . In IEEE Workshop on Motion and Video Computing,pages 5–5, 2007.
14 B. Vishnyakov, V. Gorbatsevich, S. Sidyakin, Y. Vizilter, I. Malin and A. Egorov, "Fast Moving Objects detection Using iLBP Background Model," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-3, 2014.
15 X. Sun and F. Chang, "Background Model Combining Gauss Model with Local Binary Pattern Feature," Journal of Convergence Information Technology(JCIT) Volume 7, Number 17, Sep 2012.
16 J. Yao and J.M. Odobez, "Multi-Layer Background Subtraction Based on Color and Texture," IN IEEE The CVPR Visual Surveillance Workshop (CVPR-VS), MINNEAPOLIS, June 2007.