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Real-Time Activity Recognition Technique for Surveillance Camera and Implementation on Digital Signal Processor

by R. Karthikeyan, P. Mahalakshmi, N. Gowri Shankar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 13
Year of Publication: 2013
Authors: R. Karthikeyan, P. Mahalakshmi, N. Gowri Shankar
10.5120/10695-5607

R. Karthikeyan, P. Mahalakshmi, N. Gowri Shankar . Real-Time Activity Recognition Technique for Surveillance Camera and Implementation on Digital Signal Processor. International Journal of Computer Applications. 64, 13 ( February 2013), 31-34. DOI=10.5120/10695-5607

@article{ 10.5120/10695-5607,
author = { R. Karthikeyan, P. Mahalakshmi, N. Gowri Shankar },
title = { Real-Time Activity Recognition Technique for Surveillance Camera and Implementation on Digital Signal Processor },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 13 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number13/10695-5607/ },
doi = { 10.5120/10695-5607 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:16:21.190278+05:30
%A R. Karthikeyan
%A P. Mahalakshmi
%A N. Gowri Shankar
%T Real-Time Activity Recognition Technique for Surveillance Camera and Implementation on Digital Signal Processor
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 13
%P 31-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intelligent Video Surveillance allows users to easily monitor the secure areas with surveillance cameras, and thus eliminating the need for manual work and saves the huge monitoring costs. A novel method for object tracking, based on image segmentation is proposed to automatically recognize activities around restricted area to improve safety and security of the servicing area by multiplexing hundreds of video streams in real time. Key component for the proposed system includes background learning and updating, foreground segmentation, features extraction, and decision-making process. The proposed method uses adaptive background subtraction techniques to handle illumination changes to improve the performance of the video surveillance and video-enable operations. The algorithms are simulated using MATLAB tool to verify its stability in various conditions by giving various input video samples and its output is taken as benchmark for real time implementation in DSP processor.

References
  1. M. Valera and S. A. Velastin, "Intelligent distributed surveillance systems: a review. " IEEE Proc. Vision, Image and Signal Processing, vol. 2, pp. 192-204, 2005.
  2. N. Sulman, T. Sanocki, D. Goldgof, and R. Kasturi, "How effective is human video surveillance performance?," Int. Conference Pattern Recognition, pp. 1-3, 2008.
  3. W. Hu, T. Tan, L. Wang, and S. Maybank "A survey on visual surveillance of object motion and behaviors," IEEE Trans. Systems, Man, and Cybernetics Part C, vol. 34, no. 3, pp. 334-352, 2004.
  4. Massimo Piccardi "Background subtraction techniques: a review", Computer Vision Research Group (CVRG), University of Technology, Sydney (UTS), The ARC Centre of Excellence for Autonomous Systems (CAS) Faculty of Engineering, UTS, April 15,2004
  5. J. Heikkila and O. Silven, "A real-time system for monitoring of cyclists and pedestrians," IEEE Workshop on Visual Surveillance, pp. 74-81, 1999.
  6. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," IEEE Int. Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
  7. G. Halevy and D. Weinshall, "Motion of disturbances: detection and tracking of multibody non-rigid motion," IEEE Int. Conference on Computer Vision and Patter Recognition, pp. 897-902, 1997.
  8. R. Cutler and L. Davis, "View-based detection and analysis of periodic motion," Int. Conference on Pattern Recognition, pp. 495-500, 1998.
  9. R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image change detection algorithms: a systematic survey," IEEE Trans. on Image Processing, vol. 14, no. 3, pp. 294-307, 2005.
  10. A. Dominguez-Caneda, C. Urdiales, and F. Sandoval, "Dynamic background subtraction for object extraction using virtual reality based prediction," Electrotechnical Conference (MELECON), pp. 466-469, 2006.
  11. S-C. Cheung and C. Kamath, "Robust background subtraction with foreground validation for Urban Traffic Video," EURASIP Journal on Applied Signal Processing, vol. 14, pp. 1-11, 2005.
  12. Rafael C Gonzalez, Richard E Woods, "Digital Image Processing".
Index Terms

Computer Science
Information Sciences

Keywords

Video Analytics image segmentation tracking video surveillance Background Subtraction

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