Institute of Perception, Action and BehaviourThis thesis investigates the classification of the behaviour of multiple persons when
viewed from a video camera. Work upon a constrained case of multiple person interaction
in the form of team games is investigated. A comparison between attempting
to model individual features using a (hierarchical dynamic model) and modelling the
team as a whole (using a support vector machine) is given. It is shown that for team
games such as handball it is preferable to model the whole team. In such instances
correct classification performance of over 80% are attained. A more general case of
interaction is then considered. Classification of interacting people in a surveillance
situation over several datasets is then investigated. We introduce a new feature set and
compare several methods with the previous best published method (Oliver 2000) and
demonstrate an improvement in performance. Classification rates of over 95% on real
video data sequences are demonstrated. An investigation into how the length of time a
sequence is observed is then performed. This results in an improved classifier (of over
2%) which uses a class dependent window size. The question of detecting pre/post and
actual fighting situations is then addressed. A hierarchical AdaBoost classifier is used
to demonstrate the ability to classify such situations. It is demonstrated that such an
approach can classify 91% of fighting situations correctly
