  An airport runway and taxiway (airside) area is a highly dynamic and complex
environment featuring interactions between different types of vehicles (speed
and dimension), under varying visibility and traffic conditions. Airport ground
movements are deemed safety-critical activities, and safe-separation procedures
must be maintained by Air Traffic Controllers (ATCs). Large airports with
complicated runway-taxiway systems use advanced ground surveillance systems.
However, these systems have inherent limitations and a lack of real-time
analytics. In this paper, we propose a novel computer-vision based framework,
namely "Deep4Air", which can not only augment the ground surveillance systems
via the automated visual monitoring of runways and taxiways for aircraft
location, but also provide real-time speed and distance analytics for aircraft
on runways and taxiways. The proposed framework includes an adaptive deep
neural network for efficiently detecting and tracking aircraft. The
experimental results show an average precision of detection and tracking of up
to 99.8% on simulated data with validations on surveillance videos from the
digital tower at George Bush Intercontinental Airport. The results also
demonstrate that "Deep4Air" can locate aircraft positions relative to the
airport runway and taxiway infrastructure with high accuracy. Furthermore,
aircraft speed and separation distance are monitored in real-time, providing
enhanced safety management.
