  We propose a Gaussian mixture model for background subtraction in infrared
imagery. Following a Bayesian approach, our method automatically estimates the
number of Gaussian components as well as their parameters, while simultaneously
it avoids over/under fitting. The equations for estimating model parameters are
analytically derived and thus our method does not require any sampling
algorithm that is computationally and memory inefficient. The pixel density
estimate is followed by an efficient and highly accurate updating mechanism,
which permits our system to be automatically adapted to dynamically changing
operation conditions. Experimental results and comparisons with other methods
show that our method outperforms, in terms of precision and recall, while at
the same time it keeps computational cost suitable for real-time applications.
