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Video Scene Segmentation : A Novel Method to Determine Objects

Authors(3) :-A. Syed Kalith, D. Mohanapriya, Dr. K. Mahesh

Video segmentation plays an important role in the MPEG-4 standard for multimedia applications. Segmentation of videos into their respective foreground and background articulate its importance in Video compression, human-computer interaction, video editing and manipulation etc. Video sequences are converted into frames and processing is done. The key perspective consider in this paper is the moving object detection with noise reduction. The video segmentation is done by detecting the moving objects on each frames and then labeling on it. A hybrid algorithm is proposed that quickly and efficiently extract the moving objects from the video sequence. Background difference is involved so as to acquire the initial object masking and to solve the uncovered background problem in the frames. The combination of noise reduction and background difference will yield the moving object within the video sequences with accuracy. The proposed algorithm is evaluated with varying input video sequences and results are produced. The experimental results show the method defers low computational complexity and better results in real time applications.
A. Syed Kalith, D. Mohanapriya, Dr. K. Mahesh
MPEG-4 Standard, Video Segmentation, Filtering, Noise Removal, Background Difference, Object Detection.
  1. Li Junshan, Yang Wei, Zhang Xiongmei, “Infrared image processing, analysis and integration Beijing”, Science Press. 2009.
  2. K.Mahesh, K.Kuppusamy, "A New Hybrid Algorithm for video segmentation" ,Springerlink, Advances in Computer Science, Engineering & Applications, Advances in Intelligent and Soft Computing Volume 166, 2012, pp 587-595.
  3. K.Mahesh, K.Kuppusamy, "Video Segmentation using Hybrid Segmentation Method" , European Journal of Scientific Research ISSN 1450-216X Vol.71 No.3 (2012), pp. 312-326.
  4. Agarwal L, Lakhwani K, Optimization of frame rate in real time object detection and tracking, International Journal of Scientific & Technology Research, Volume 2, Issue 7, July 2013, pp:132-134.
  5. Beevi C.P.Y., Natarajan S. (2009) A Novel Video Segmentation Algorithm with Shadow Cancellation and Adaptive Threshold Techniques. In: ?l?zak D., Pal S.K., Kang BH., Gu J., Kuroda H., Kim T. (eds) Signal Processing, Image Processing and Pattern Recognition. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg
  6. B. Deori, D. M. Thounaojam, "A survey on moving object tracking in video", International Journal on Information Theory (IJIT), vol. 3, no. 3, July 2014.
  7. Mengxin Li, Jingjing Fan, Ying Zhang, RuiZhang, Weijing Xu and Dingding Hou,“Moving Object Detection and TrackingAlgorithm”, in TELKOMNIKA, Vol. 11,No. 10, October 2013, pp. 5539 ? 5544
  8. Sudhanshu Sinha, Manohar Mareboyana, “Video Segmentation into Background and Foreground Using Simplified Mean Shift Filter and K-Means
  9. Saad A. Yaseen and Sreela Sasi, "Robust Algorithm for Object Detection and Tracking in a Dynamic Scene," Journal of Image and Graphics, Vol. 2, No. 1, pp. 41-45, June 2014. doi: 10.12720/joig.2.1.41-45.
  10. ?Tripty Singh, Sanju S and Bichu Vijay, “ANew Algorithm Designing for Detection ofMoving Objects in Video”, in InternationalJournal of Computer Applications (0975 ?8887) Volume 96? No.2, June 2014.
  11. ?Mada Amarnadh, S. Asif Hussain, M. Janardhana Raju, “A Review of Video Segmentation Techniques”, Global Journal of Advanced Engineering Technologies, Special Issue (CTCNSF-2014).
  12. D. Mohanapriya and Dr. K. Mahesh., Robust Video Tracking System with Shadow Suppression Based on Feature Extraction. Aust. J. Basic & Appl. Sci., 10 (12): 307-311, 2016
  13. N. S. Nagaraja, F. Schmidt, and T. Brox. Video segmentation with just a few strokes. In ICCV, 2015
  14. C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang. Hierarchical convolutional features for visual tracking. In ICCV, 2015
  15. L. Xu, J. Chen, and J. Jia. A segmentation based variational model for accurate optical flow estimation. In ECCV, 2008
  16. ?D. Zhang, O. Javed, and M. Shah. Video object segmentation through spatially accurate and temporally dense extraction of primary object regions 2013.
  17. D.Mohanapriya,Dr.K.Mahesh “A Comparative Analysis of Video Tracking Techniques” International Journal for Modern Trends in Science and Technology, Volume: 03, Issue No: 05, May 2017.
  18. Mohanapriya D. & Mahesh K. Multimed Tools Appl (2017) 76: 25731. https://doi.org/10.1007/s11042-017-4409-3.
  19. Mohanapriya, D., and K. Mahesh. “A video target tracking using shadow suppression and feature extraction.” 2017 International Conference on Information Communication and Embedded Systems (ICICES), 2017, doi:10.1109/icices.2017.8070734.
  20. Mohanapriya, D., and K. Mahesh. “A SURVEY ON VIDEO OBJECT TRACKING SYSTEM.” International Journal of Advanced Research Trends in Engineering and Technology (IJARTET), vol. 3, no. 20, Apr. 2016, pp. 474?479.
Publication Details
  Published in : Volume 4 | Issue 2 | January-February 2018
  Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 90-94
Manuscript Number : IJSRST184121
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
A. Syed Kalith, D. Mohanapriya, Dr. K. Mahesh, "Video Scene Segmentation : A Novel Method to Determine Objects", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 2, pp.90-94, January-February-2018
URL : http://ijsrst.com/IJSRST184121