Research Article Open Access

Using Multi-Scale Filtering to Initialize a Background Extraction Model

S. H. Davarpanah1, Fatimah Khalid1, N. A. Lili1, S. S. Puteri1 and M. Golchin1
  • 1 Universiti Putra Malaysia, Malaysia

Abstract

Problem statement: Probability-based methods which usually work based on the saved history of each pixel are utilized severally in extracting a background image for moving detection systems. Probability-based methods suffer from a lack of information when the system first begins to work. The model should be initialized using an alternative accurate method. Approach: The use of a nonparametric filtering to calculate the most probable value for each pixel in the initialization phase can be useful. In this study a complete system to extract an adaptable gray scale background image is presented. It is a probability-based system and especially suitable for outdoor applications. The proposed method is initialized using a multi-scale filtering method. Results: The results of the experiments certify that not only the quality of the final extracted background is about 10% more accurate in comparison to four recent re-implemented methods, but also the time consumption of the extraction are acceptable. Conclusion: Using multi-scale filtering to initialize the background model and to extract the background using a probability-based method proposes an accurate and adaptable background extraction method which is able to handle sudden and large illumination changes.

Journal of Computer Science
Volume 8 No. 7, 2012, 1077-1084

DOI: https://doi.org/10.3844/jcssp.2012.1077.1084

Submitted On: 29 March 2011 Published On: 24 May 2012

How to Cite: Davarpanah, S. H., Khalid, F., Lili, N. A., Puteri, S. S. & Golchin, M. (2012). Using Multi-Scale Filtering to Initialize a Background Extraction Model. Journal of Computer Science, 8(7), 1077-1084. https://doi.org/10.3844/jcssp.2012.1077.1084

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Keywords

  • Adaptive background extraction
  • background modelling
  • probability-based method
  • multi-scale filtering
  • non-parametric method
  • moving object detection
  • outdoor applications
  • history-based method