This paper describes the real-time implementation of a simple and robust mo
tion detection algorithm based on Markov random field (MRF) modeling. MRF-b
ased algorithms often require a significant amount of computations. The int
rinsic parallel property of MRF modeling has led most of implementations to
ward parallel machines and neural networks, but none of these approaches of
fers an efficient solution for real-world (i.e., industrial) applications.
Here, an alternative implementation for the problem at hand is presented yi
elding a complete, efficient and autonomous real-time system for motion det
ection. This system is based on a hybrid architecture, associating pipeline
modules with one asynchronous module to perform the whole process, from vi
deo acquisition to moving object masks visualization. A board prototype is
presented and a processing rate of 15 images/s is achieved, showing the val
idity of the approach.