Real-time segmentation of moving regions in image sequences is a fundamental step in many
vision systems including automated visual surveillance, human-machine interface, and very low-bandwidth
telecommunications. A typical method is background subtraction. Many background models have been
introduced to deal with different problems. One of the successful solutions to these problems is to use a
multi-colour background model per pixel proposed by Grimson et al [1,2,3]. However, the method suffers
from slow learning at the beginning, especially in busy environments. In addition, it can not distinguish
between moving shadows and moving objects. This paper presents a method which improves this adaptive
background mixture model. By reinvestigating the update equations, we utilise different equations at
different phases. This allows our system learn faster and more accurately as well as adapt effectively to
changing environments. A shadow detection scheme is also introduced in this paper. It is based on a
computational colour space that makes use of our background model. A comparison has been made
between the two algorithms. The results show the speed of learning and the accuracy of the model using
our update algorithm over the Grimson et al’s tracker. When incorporate with the shadow detection, our
method results in far better segmentation than that of Grimson et al.
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