Optimizing and Background Learning in a Single Process of Moving Object Detection

Authors(3) :-R. Usha, S. Yamuna, R. Deepa

Video surveillance systems have long been in use to monitor security sensitive areas. The making of video surveillance systems "smart" requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. Moving object detection is the basic step for further analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes such as human, vehicle, animal, clutter, etc. It is necessary to distinguish objects from each other in order to track and analyse their actions reliably. In previous system performed background subtraction by using Canny Edge Detection. In Canny Edge Detection process we are taking two images for comparison those are background image and foreground image.

Authors and Affiliations

R. Usha
Computer science and engineering, Prince Dr. K. Vasudevan College of Engineering& Technology, Ponmar, Tamilnadu, India
S. Yamuna
Computer science and engineering, Prince Dr. K. Vasudevan College of Engineering& Technology, Ponmar, Tamilnadu, India
R. Deepa
Computer science and engineering, Prince Dr. K. Vasudevan College of Engineering& Technology, Ponmar, Tamilnadu, India

Object Detection, GDSM, DP-GMM

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Publication Details

Published in : Volume 2 | Issue 2 | March-April 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 389-393
Manuscript Number : CSEIT1722107
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

R. Usha, S. Yamuna, R. Deepa, "Optimizing and Background Learning in a Single Process of Moving Object Detection ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.389-393, March-April-2017.
Journal URL : http://ijsrcseit.com/CSEIT1722107

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