  We propose a system that uses video as the input to track the position of
objects relative to their surrounding environment in real-time. The neural
network employed is trained on a 100% synthetic dataset coming from our own
automated generator. The positional tracker relies on a range of 1 to n video
cameras placed around an arena of choice.
  The system returns the positions of the tracked objects relative to the
broader world by understanding the overlapping matrices formed by the cameras
and therefore these can be extrapolated into real world coordinates.
  In most cases, we achieve a higher update rate and positioning precision than
any of the existing GPS-based systems, in particular for indoor objects or
those occluded from clear sky.
