  Detection of moving objects in videos is a crucial step towards successful
surveillance and monitoring applications. A key component for such tasks is
called background subtraction and tries to extract regions of interest from the
image background for further processing or action. For this reason, its
accuracy and real-time performance is of great significance. Although,
effective background subtraction methods have been proposed, only a few of them
take into consideration the special characteristics of thermal imagery. In this
work, we propose a background subtraction scheme, which models the thermal
responses of each pixel as a mixture of Gaussians with unknown number of
components. Following a Bayesian approach, our method automatically estimates
the mixture structure, while simultaneously it avoids over/under fitting. 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. We propose a reference implementation of our
method in reconfigurable hardware achieving both adequate performance and low
power consumption. Adopting a High Level Synthesis design, demanding floating
point arithmetic operations are mapped in reconfigurable hardware;
demonstrating fast-prototyping and on-field customization at the same time.
