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A Real-time SoC Design of Foreground Object Segmentation  

Kim Ji-Su (School of Electrical Engineering and Computer Science, Seoul National University)
Lee Tae-Ho (School of Electrical Engineering and Computer Science, Seoul National University)
Lee Hyuk-Jae (School of Electrical Engineering and Computer Science, Seoul National University)
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Abstract
Recently developed MPEG-4 Part 2 compression standard provides a novel capability to handle arbitrary video objects. To support this capability, an efficient object segmentation technique is required. This paper proposes a real-time algorithm for foreground object segmentation in video sequences. The proposed algorithm consists of two steps: the first step that segments a video frame into multiple sub-regions using Spatio-Temporal Watershed Transform and the second step in which a foreground object segment is extracted from the sub-regions generated in the first step. For real-time processing, the algorithm is partitioned into hardware and software parts so that computationally expensive parts are off-loaded from a processor and executed by hardware accelerators. Simulation results show that the proposed implementation can handle QCIF-size video at 15 fps and extracts an accurate foreground object.
Keywords
MPEG-4; Hardware/software partitioning; Foreground object segmentation; Watershed transform; Real-time processing;
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1 L. Vincent and P. Soille, 'Watersheds in digital spaces: An efficient algorithm based on immersion simulations,' IEEE Trans. Pattern Anal. Machine Intell., Vol. 13, No. 6, pp. 583-598, June 1991   DOI   ScienceOn
2 L. Vincent and P. Soille, 'Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms,' IEEE Trans. Image Processing, Vol. 2, No. 2, pp. 176-201, Apr. 1993   DOI   ScienceOn
3 S. Chien, Y. Huang, B. Hsieh, S. Ma, and L. Chen, 'Fast video segmentationalgorithm with shadow cancellation, global motion compensation, and adaptive threshold techniques,' IEEE Trans. Multimedia, vol. 6, pp. 732-748, Oct. 2004   DOI   ScienceOn
4 D. Wang, 'Unsupervised video segmentation based on watersheds and temporal tracking,' IEEE Trans. Circuits Syst. Video Technol., vol. 8, pp. 539-546, Sept. 1998   DOI   ScienceOn
5 E. Hayman and J. Eklundh, 'Statistical background subtraction for a mobile observer,' in Proc. IEEE Int. Conf. Computer Vision, Oct. 2003, pp. 67-74   DOI
6 S. Lee, C. Ouyang, and S.Du, 'A neuro-fuzzy approach for segmentation of human objects in image sequences,' IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 33, pp. 420-437, June 2003   DOI   ScienceOn
7 H. Xu, Younis, A.A Younis, and M.R. Kabuka, 'Automatic moving object extraction for content-based applications,' IEEE Trans. Circuits Syst. Video Technol., vol.14, pp. 796-812, June 2004   DOI   ScienceOn
8 Y. Tsai, C. Lai, Y. Hung, and Z.Shih, 'A Bayesian approach to video object segmentation via merging 3-D watershed volumes,' IEEE Trans. Circuits Syst. Video Technol., vol. 15, pp. 175-180, Jan. 2005   DOI   ScienceOn
9 ISO/IEC 14496-2, Coding of audio-visual objects Part 2: Visual, 2001
10 F. Meyer, 'Color image segmentation,' in Proc. Int. Conf. on Image Processing and its Applications, pp. 303-306, 1992
11 P. Salembier and M. Pardas, 'Hierarchical morphological segmentation for image sequence coding,' IEEE Trans. Image Processing, Vol. 3, No. 5, Sept. 1994   DOI   ScienceOn