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

2026-07-07

AI Summary

Existing video models struggle to accurately recognize critical actions—such as drawing a weapon—in high-risk, highly dynamic body-worn police camera scenarios, lacking fine-grained understanding of real-world law enforcement interactions. To address this gap, this work introduces the first egocentric video benchmark tailored to high-stakes policing contexts, constructed from rigorously curated real officer-civilian encounters. The dataset features second-level annotations of rare yet pivotal enforcement behaviors and includes classification and multiple-choice question-answering tasks to evaluate model performance under conditions of intense motion and complex contextual dynamics. Experiments reveal that even state-of-the-art video foundation models, such as Gemini 2.5 Pro, exhibit significant shortcomings on these tasks, underscoring the benchmark’s difficulty and establishing a technical foundation for efficient human-in-the-loop review of执法 footage.

Abstract

We introduce EgoPolice, a carefully curated dataset of real, egocentric police-civilian interactions, sourced from publicly available body-worn camera videos. We select police-civilian action labels that are critical for police behavioral research and annotate them at a second-by-second granularity. The videos feature rapid and irregular camera motion, dense human interactions, and rare high-stakes events, making the dataset a challenging benchmark for motion-robust and context-aware egocentric perception. We provide two different tasks, classification and multiple-choice question-answering, and benchmark both open-source and closed-source models. We find that even the best video models like Gemini 2.5 Pro still struggle to accurately predict high-risk actions such as "Weapon Out". Beyond serving as a benchmark, EgoPolice provides a foundation for developing models capable of identifying events of interest in large-scale body-worn camera video repositories, enabling more efficient downstream human review.

Problem

• egocentric video understanding
• police body-worn camera
• high-stakes events
• action recognition
• video benchmark

Innovation

• egocentric video understanding
• police body-worn camera
• high-stakes event detection
• motion-robust perception
• fine-grained action annotation