  Semantic segmentation has attracted a large amount of attention in recent
years. In robotics, segmentation can be used to identify a region of interest,
or \emph{target area}. For example, in the RoboCup Standard Platform League
(SPL), segmentation separates the soccer field from the background and from
players on the field. For satellite or vehicle applications, it is often
necessary to find certain regions such as roads, bodies of water or kinds of
terrain. In this paper, we propose a novel approach to real-time target area
segmentation based on a newly designed spatial temporal network. The method
operates under domain constraints defined by both the robot's hardware and its
operating environment . The proposed network is able to run in real-time,
working within the constraints of limited run time and computing power. This
work is compared against other real time segmentation methods on a dataset
generated by a Nao V6 humanoid robot simulating the RoboCup SPL competition. In
this case, the target area is defined as the artificial grass field. The method
is also tested on a maritime dataset collected by a moving vessel, where the
aim is to separate the ocean region from the rest of the image. This dataset
demonstrates that the proposed model can generalise to a variety of vision
problems.
