The reviewed record of science sign in
Pith

arxiv: 2504.20007 · v3 · pith:2K4373OG · submitted 2025-04-28 · cs.AI · cs.CV

Towards AI-Driven Policing: Interdisciplinary Knowledge Discovery from Police Body-Worn Camera Footage

Anita Srbinovska , Angela Srbinovska , Vivek Senthil , Adrian Martin , John McCluskey , Jonathan Bateman , Ernest Fokou\'e This is my paper

Reviewed by Pithpith:2K4373OGopen to challenge →

classification cs.AI cs.CV
keywords policefootagetechniquesbody-worncameradatadiscoveryframework
0
0 comments X
read the original abstract

This paper proposes a novel interdisciplinary framework for analyzing police body-worn camera (BWC) footage from the Rochester Police Department (RPD) using advanced artificial intelligence (AI) and statistical machine learning (ML) techniques. Our goal is to detect, classify, and analyze patterns of interaction between police officers and civilians to identify key behavioral dynamics, such as respect, disrespect, escalation, and de-escalation. We apply multimodal data analysis by integrating image, audio, and natural language processing (NLP) techniques to extract meaningful insights from BWC footage. The framework incorporates speaker separation, transcription, and large language models (LLMs) to produce structured, interpretable summaries of police-civilian encounters. We also employ a custom evaluation pipeline to assess transcription quality and behavior detection accuracy in high-stakes, real-world policing scenarios. Our methodology, computational techniques, and findings outline a practical approach for law enforcement review, training, and accountability processes while advancing the frontiers of knowledge discovery from complex police BWC data.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    cs.CV 2026-07 conditional novelty 7.0

    EgoPolice introduces a 185-hour annotated police body-worn camera benchmark showing state-of-the-art video models fail on high-stakes actions due to motion, occlusion, and low inter-class visual separability.

  2. DeEscalWild: A Real-World Benchmark for Automated De-Escalation Training with SLMs

    cs.CL 2026-03 unverdicted novelty 7.0

    DeEscalWild supplies 1,500 high-fidelity de-escalation scenarios that let fine-tuned 3B SLMs outperform general-purpose larger models on realism and dialogue metrics.

  3. Visual Timelines of Police Encounters in Body-Worn Camera Footage: Operational Context and Activity Cataloging for Training and Analysis in OpenBWC

    cs.CV 2026-05 unverdicted novelty 5.0

    A pipeline that converts body-worn camera footage into labeled visual timelines by classifying 10-second windows along operational-context and motion-intensity axes using CLIP and optical-flow features.

  4. Ontology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reports

    cs.CL 2026-05 unverdicted novelty 3.0

    A symbolic system extracts events from 450 property crime reports, with 54.1% high-confidence outputs, 93.7% mapped via PropBank-VerbNet-WordNet, and 100% human agreement on incident initiation, stolen items, and temp...