Abstract—This paper describes the real-time implementation
of a simple and robust motion detection algorithm based on
Markov random ?eld (MRF) modeling. MRF-based algorithms
often require a signi?cant amount of computations. The intrinsic
parallel property of MRF modeling has led most of implementa-
tions toward parallel machines and neural networks, but none of
these approaches offers an ef?cient solution for real-world (i.e.,
industrial) applications. Here, an alternative implementation for
the problem at hand is presented yielding a complete, ef?cient and
autonomous real-time system for motion detection. This system
is based on a hybrid architecture, associating pipeline modules
with one asynchronous module to perform the whole process,
from video acquisition to moving object masks visualization. A
board prototype is presented and a processing rate of 15 images/s
is achieved, showing the validity of the approach.
Index Terms—Digital signal processor (DSP), Markov random
?eld (MRF), motion detection, real-time implementation.