  Future airports are becoming more complex and congested with the increasing
number of travellers. While the airports are more likely to become hotspots for
potential conflicts to break out which can cause serious delays to flights and
several safety issues. An intelligent algorithm which renders security
surveillance more effective in detecting conflicts would bring many benefits to
the passengers in terms of their safety, finance, and travelling efficiency.
This paper details the development of a machine learning model to classify
conflicting behaviour in a crowd. HRNet is used to segment the images and then
two approaches are taken to classify the poses of people in the frame via
multiple classifiers. Among them, it was found that the support vector machine
(SVM) achieved the most performant achieving precision of 94.37%. Where the
model falls short is against ambiguous behaviour such as a hug or losing track
of a subject in the frame. The resulting model has potential for deployment
within an airport if improvements are made to cope with the vast number of
potential passengers in view as well as training against further ambiguous
behaviours which will arise in an airport setting. In turn, will provide the
capability to enhance security surveillance and improve airport safety.
