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.

本研究针对警察佩戴摄像机录像的分析，提出了一种新的跨学科框架，填补了现有研究在警务与人工智能结合领域的空白。通过整合视频、音频和自然语言处理等多模态数据分析技术，该研究识别出了警务人员与平民互动中的关键行为动态，具有重要的实际应用价值。

面向人工智能驱动的警务：来自警察佩戴摄像机录像的跨学科知识发现