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

Garbage in, garbage out: Zero-shot detection of crime using Large Language Models