OmniEye: Efficient Multimodal Forensic Video Intelligence for Law-Enforcement Body-Worn Cameras

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

arXiv · 2609.09460

OmniEye: Efficient Multimodal Forensic Video Intelligence for Law-Enforcement Body-Worn Cameras

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

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Abstract

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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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. 2026-09-08. OmniEye: Efficient Multimodal Forensic Video Intelligence for Law-Enforcement Body-Worn Cameras. https://arxiv.org/abs/2609.09460

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