EgoPolice: A Benchmark for Egocentric Video Understanding in High-Stakes Police Body-Worn Camera Footage

We introduce EgoPolice, a carefully curated dataset of real,egocentric police–civilian interactions, sourced from publicly availablebody-worn camera videos. We select police-civilian action labels that arecritical for police behavioral research and annotate them at a second-by-second granularity. The videos feature rapid and irregular cameramotion, dense human interactions, and rare high-stakes events, mak-ing the dataset a challenging benchmark for motion-robust and context-aware egocentric perception. We provide two different tasks, classifica-tion and multiple-choice question-answering, and benchmark both open-source and closed-source models. We find that even the best video modelslike Gemini 2.5 Pro still struggle to accurately predict high-risk actionssuch as “Weapon Out”. Beyond serving as a benchmark, EgoPolice pro-vides a foundation for developing models capable of identifying eventsof interest in large-scale body-worn camera video repositories, enablingmore efficient downstream human review.Content Warning: This paper includes real police body-worn camerafootage, including potentially distressing scenes.