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Reseach Article

Automatic Segmentation of Moving Object in Video Sequences

Published on July 2016 by Shubhangi Vaikole, S. D. Sawarkar
International Conference on Communication Computing and Virtualization
Foundation of Computer Science USA
ICCCV2016 - Number 1
July 2016
Authors: Shubhangi Vaikole, S. D. Sawarkar
78fba3d2-56a0-44d6-a0db-94750a35e09d

Shubhangi Vaikole, S. D. Sawarkar . Automatic Segmentation of Moving Object in Video Sequences. International Conference on Communication Computing and Virtualization. ICCCV2016, 1 (July 2016), 0-0.

@article{
author = { Shubhangi Vaikole, S. D. Sawarkar },
title = { Automatic Segmentation of Moving Object in Video Sequences },
journal = { International Conference on Communication Computing and Virtualization },
issue_date = { July 2016 },
volume = { ICCCV2016 },
number = { 1 },
month = { July },
year = { 2016 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icccv2016/number1/912-1649/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication Computing and Virtualization
%A Shubhangi Vaikole
%A S. D. Sawarkar
%T Automatic Segmentation of Moving Object in Video Sequences
%J International Conference on Communication Computing and Virtualization
%@ 2249-0868
%V ICCCV2016
%N 1
%P 0-0
%D 2016
%I International Journal of Applied Information Systems
Abstract

In content based video retrieval and concept detection systems video segmentation is the most important step. There are basically two methods for video segmentation, one is semiautomatic and other is automatic. A lot of work is already performed on this two approaches. Semiautomatic methodsrequires the user intervention to draw the boundary of object. Many applications require automatic segmentation methods but still there is a lot of scope for research because mostly the methods are application specific. The main focus of this paper is to identify the gaps that are present in the existingvideo segmentation system and also to provide the possible solutions to overcome those gaps so that the accurate and efficient system which can segment objects in video can be developed. The proposed system aims to resolve the issue of uncovered background, Temporary poses and Global motion of background.

References
  1. Shao-Yi Chien, Yu-Wen Huang, Bing-Yu Hsieh, Shyh-YihMa, and Liang-Gee Chen,"Fast Video Segmentation algorithm with Shadow Cancellation, Global Motion compensation, and Adaptive Threshold Techniques,"IEEE Trans. on Circuits and System for VideoTechnology. , vol. 6, pp. 732- 748, no. 5, Oct. 2004.
  2. Dong Zhang1, Omar Javed2, Mubarak Shah1," VideoObject Segmentation through Spatially Accurate andTemporally Dense Extraction of Primary Object Regions," 2013 IEEE Conference on Computer Vision and Pattern Recognition
  3. Camille Couprie,"Causal Graph based videosegmentation"(2012)
  4. McFralane, N. J. B. and Schofield, C. P. "Segmentation and tracking of piglets in images". Machine Vision and Applications, Vol. 8, No. 3, 187-193. 2005.
  5. Ricardo Augusto Castellanos Jimenez "Event Detection In Surveillance Video" FloridaAtlantic UniversityBoca Raton, Florida May 2010
  6. Shao-Yi Chien, Shyh-Yih Ma, and Liang-Gee Chen, "Efficient Moving Object Segmentation Algorithm Using Background Registration Technique," IEEE Trans. on Circuits Syst. Video Technol. , vol. 12, no. 7, pp. 577-586, 2002.
  7. Tung-Chien Chen "Video Segmentation Based on Image Change Detection for Surveillance Systems".
  8. Dong Zhang, Omar Javed, Mubarak Shah," Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions," 2013 IEEE Conference on Computer Vision and Pattern Recognition
  9. T. Ma and L. Latecki. Maximum weight cliques with mutex constraints for video object segmentation. In CVPR, pages 670–677, 2012.
  10. Y. Lee, J. Kim, and K. Grauman. Key-segments for video object segmentation. In ICCV, pages 1995–2002, 2011.
  11. D. Tsai, M. Flagg, and J. Rehg. Motion coherent tracking with multi-label mrf optimization. In BMVC, page 1, 2010.
  12. H. Jiang, A. S. Helal, A. K. Elmagarmid, and A. Joshi. "Scene change detection techniques for video database systems". multimedia Systems, 6(3):186–195.
  13. A. Dailianas, R. B. Allen, and P. England. Comparison of automatic video segmentation algorithms". In SPIE Conference on Integration Issues in Large Commercial Media Delivery Systems, volume 2615, pages 2–16, Philadelphia, PA
  14. M. K. Mandal, F. Idris, and S. Panchanathan" A critical evaluation of image and video indexing techniques in the compressed domain". Image and Vision Computing, 17(7):513–529.
  15. S. Y. Chien, Y. W. Huang, and L. G. Chen, "Predictive watershed: a fast watershed algorithm for video segmentation," IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, May 2003, Page(s):453-461
  16. R. Zabih, J. Miler, K. Mai, "A feature-based algorithm for detecting and classifying production ejects", Multimedia Systems 7 (1999) 119}128.
  17. K. Zhang and J. Kittler, "Using background memory for efficient video coding," in Proc. IEEE Int. Conf. Image Processing, 1998, pp. 944–947.
  18. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision. Reading, MA: Addison-Wesley, 1992, pp. 28–48.
  19. Chen T-H, Liau H-S and Chiou Y-C. (2005) "An Efficient Video Object Segmentation Algorithm Based on Change Detection and Background Updating. " Kun Shan University,National Computer Symposium, MIA1-2 (MI14).
Index Terms

Computer Science
Information Sciences

Keywords

Global Motion of Background (GMOB) Semiautomatic segmentation affine model.