Abstract—This paper describes the real-time implementation of a simple and robust motion detection algorithm based on Markov random field (MRF) modeling. MRF-based algorithms often require a significant 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 efficient solution for real-world (i.e., industrial) applications. Here, an alternative implementation for the problem at hand is presented yielding a complete, efficient 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 field (MRF), motion detection, real-time implementation.