Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


April Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the April 2021 Edition of the journal. The last date of research paper submission is March 15, 2021.

Automatic Segmentation of Moving Object in Video Sequences

Shubhangi Vaikole, S. D. Sawarkar Published in Pattern Recognition

IJAIS Proceedings on International Conference on Communication Computing and Virtualization
Year of Publication: 2016
© 2015 by IJAIS Journal
Download full text
  1. Shubhangi Vaikole and S D Sawarkar. Article: Automatic Segmentation of Moving Object in Video Sequences. IJAIS Proceedings on International Conference on Communication Computing and Virtualization ICCCV 2016(1):6-10, July 2016. BibTeX

    	author = "Shubhangi Vaikole and S. D. Sawarkar",
    	title = "Article: Automatic Segmentation of Moving Object in Video Sequences",
    	journal = "IJAIS Proceedings on International Conference on Communication Computing and Virtualization",
    	year = 2016,
    	volume = "ICCCV 2016",
    	number = 1,
    	pages = "6-10",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"


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.


  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).


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