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

Authors

  • 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

Keywords:

Object Detection, GDSM, DP-GMM

Abstract

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.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-04-30

Issue

Volume 2, Issue 2, March-April-2017

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R. Usha, S. Yamuna, R. Deepa, " Optimizing and Background Learning in a Single Process of Moving Object Detection , IInternational 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.