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 video, audio, and natural language processing (NLP) techniques to extract meaningful insights from BWC footage. We present our methodology, computational techniques, and findings, outlining a practical approach for law enforcement while advancing the frontiers of knowledge discovery from police BWC data.
View on arXiv@article{srbinovska2025_2504.20007,
title={ Towards AI-Driven Policing: Interdisciplinary Knowledge Discovery from Police Body-Worn Camera Footage },
author={ Anita Srbinovska and Angela Srbinovska and Vivek Senthil and Adrian Martin and John McCluskey and Ernest Fokoué },
journal={arXiv preprint arXiv:2504.20007},
year={ 2025 }
}