Automatic analysis of composite activities in video sequences using Key Action Discovery and hierarchical graphical models

Modelling human activities as temporal sequences of their constituent actions has been the object of much research effort in recent years. However, most of this work concentrates on tasks where the action vocabulary is relatively small and/or each activity can be performed in a limited number of ways. In this work, we propose a novel and robust framework for analysing prolonged activities arising in tasks which can be effectively achieved in a variety of ways, which we name mid-term activities. We show that we are able to efficiently analyse and recognise such activities and also detect potential errors in their execution. To achieve this, we introduce an activity classification method which we name the Key Action Discovery system. We demonstrate that this method combined with temporal modelling of activities´ constituent actions with the aid of hierarchical graphical models offers higher classification accuracy compared to current activity identification schemes.