CFP last date
22 April 2024
Reseach Article

Real Time Motion Detected Video Storage Algorithm for Online Video Recording

Published on March 2012 by Sagar Badnerkar, Yash Kshirsagar
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 3
March 2012
Authors: Sagar Badnerkar, Yash Kshirsagar
55a0ab50-3811-4d5a-aa58-5828d3273bb2

Sagar Badnerkar, Yash Kshirsagar . Real Time Motion Detected Video Storage Algorithm for Online Video Recording. International Conference in Computational Intelligence. ICCIA, 3 (March 2012), 21-25.

@article{
author = { Sagar Badnerkar, Yash Kshirsagar },
title = { Real Time Motion Detected Video Storage Algorithm for Online Video Recording },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 3 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 21-25 },
numpages = 5,
url = { /proceedings/iccia/number3/5108-1020/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Sagar Badnerkar
%A Yash Kshirsagar
%T Real Time Motion Detected Video Storage Algorithm for Online Video Recording
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 3
%P 21-25
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper, a real time background modelling and its maintenance for motion activated online video recording along with compression of video is described. maintenance model is proposed for preventing some kind of false triggering such as illumination changes environmental condition changes etc. Detection of changes in a background frame initiates video recording with real time video compression reduce the size of memory. Real time video compression using Three Dimensional Discrete Cosine Transform is used which takes advantage of statistical behaviour of video data both in spatial and temporal domain. High degree of compression is a 3D-DCT technique without using motion estimation and compensation techniques.

References
  1. Ching-Kai Huang and Tsuhan Chen, “Motion Activated Video Surveillance Using TI DSP” DSPS FEST '99, Houston, Texas, August 4-6, 1999
  2. Weiqiang Wang, Datong Chen, Wen Gao and Jie Yang, “ Modeling Background from Compressed Video”, Second Joint IEEE International Workshop on Video Surveillance and Performance Evaluation of Tracking and Surveillance.
  3. D. Gutchess, M. Trajkovi´c, E. Cohen-Solal, D. Lyons, A. K. Jain “A Background Model Initialization Algorithm for Video Surveillance” 0-7695-1143-0/01 $10.00 (C) 2001 IEEE
  4. R.Westwater and B.Furht,”Real-Time Video Copression: Techniques and Algorithms”,Kluwer Academic Publishers, Norwell.MA,1996.
  5. M.Bakr and A.E. Salama,“Implementation of 3D-DCT Based Video Encoder/Decoder System” @2002 IEEE
  6. R.Westwater and B.Furht, “The XYZ Algorithm for Real Time Compression of Full Motion Video”,Journal of Real Time Imaging,Vol.2,No.1,February 1996,pp.19-34
  7. R.Westwater and B.Furht,”Three Dimensional DCT Video Compression Technique Based on Adaptive Quantizers” 1996 IEEE
  8. M.Bakr and A.E. Salama,“Implementation of 3D-DCT Based Video Encoder/Decoder System” @2002 IEEE
  9. Rohini Nagapadma and Narasimha Kaulgud, “Three Dimensional Motion Vector less Compression” IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.4, April 2009
  10. Borko Furt,Ken Gustafson, Hesong Huang and Oge Marques, “An Adaptive Three Dimensional DCT Compression Based on Motion Analysis”
  11. Servais, M de Jager, G,”Video Compression Using the three dimensional discrete cosine transform (3D-DCT) “1997IEEE
  12. Shireen Y. Elhabian, Khaled M. El-Sayed and Sumaya H. Ahmed, “Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art
Index Terms

Computer Science
Information Sciences

Keywords

background modelling video compression motion estimation 3D-DCT