A Logic Programming Approach to Activity Recognition
  We have been developing a system for recognising human activity given a
symbolic representation of video content. The input of our system is a set of
time-stamped short-term activities detected on video frames. The output of our
system is a set of recognised long-term activities, which are pre-defined
temporal combinations of short-term activities. The constraints on the
short-term activities that, if satisfied, lead to the recognition of a
long-term activity, are expressed using a dialect of the Event Calculus. We
illustrate the expressiveness of the dialect by showing the representation of
several typical complex activities. Furthermore, we present a detailed
evaluation of the system through experimentation on a benchmark dataset of
surveillance videos.
