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arXiv Computer Vision By Saverio Cavasin, Pietro Tedeschi, Mattia Tamiazzo, Alessandro Brighente, Simone Milani, Mauro Conti

VESSI - VLM-Enhanced Support for Surveillance and Investigations

Read the original on arXiv Computer Vision →

VESSI is a Vision‑Language Model (VLM) framework that enhances automated video surveillance by converting salient visual features into textual descriptions for prompt‑driven interrogation. The authors evaluate four state‑of‑the‑art VLMs and introduce the Composite Model Utility Score (CMUS) to assess performance on largely unlabeled datasets. Results show that the best model flags relevant activity in over 66% of videos while cutting review time by more than 85%, and the CMUS ordering aligns with manual false‑positive rates, supporting its operational use.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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