PAPER / ARXIV:2609.09460
Mamadou K. Keita , Angela Srbinovska , Anita Srbinovska , Nishka Desai , Isabella Zicari , P. Kwaku Sanaah-Faried , Sanjay Charitesh Makam , Wyatt Auten , Vivek Senthil , Hannah Desnick , Jonathan Bateman , Adrian Martin , Christopher Homan , John McCluskey , Ernest Fokoué
RESUMO
We introduce OmniEye, a multimodal video intelligence system for law-enforcement training and review (source code available on request to verified law-enforcement and public-safety agencies). OmniEye ingests body-worn camera footage and perceives every 30-second window jointly across video and audio with one multimodal foundation model. It then stores the model's structured output in an embedded SQLite database with BM25 full-text search. Officers can question the footage through an agent that writes structured queries, retrieves candidate windows, and re-perceives them with the model before it may cite them. The whole system runs on one 16 GB GPU with a 4-bit quantization-aware-trained model, and it also scales to full bf16 precision on a multi-GPU cluster.
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