  This paper proposes exploiting the common sense knowledge learned by large
language models to perform zero-shot reasoning about crimes given textual
descriptions of surveillance videos. We show that when video is (manually)
converted to high quality textual descriptions, large language models are
capable of detecting and classifying crimes with state-of-the-art performance
using only zero-shot reasoning. However, existing automated video-to-text
approaches are unable to generate video descriptions of sufficient quality to
support reasoning (garbage video descriptions into the large language model,
garbage out).
