On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection

  • Authors:

  • Mahfuzul Haque;Manzur Murshed;Manoranjan Paul

  • Affiliations:

  • -;-;-

  • Venue:
  • AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
  • Year:
  • 2008

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Abstract

Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences.