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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 techniquedoi:10.1109/avss.2008.12 dblp:conf/avss/HaqueMP08 fatcat:b7xnilptwba7ji5mynyvgkfdq4