用AI分析警察执法录像，识别互动中的尊重与冲突行为。
Towards AI-Driven Policing: Interdisciplinary Knowledge Discovery from Police Body-Worn Camera Footage
- 融合图像、音频、文本多模态分析，提取执法互动特征。
- 通过语音分离与大模型生成结构化摘要，准确识别行为模式。
- 适用于警务监督、培训改进，推动执法透明化研究。
本文提出一种新型跨学科框架，利用先进人工智能与统计机器学习技术分析罗切斯特警察局（RPD）的警察执法记录仪（BWC）视频数据。目标是检测、分类并分析警察与民众互动中的行为动态，如尊重、不尊重、升级与降级等。通过整合图像、音频和自然语言处理（NLP）技术，实现多模态数据分析。框架包含说话人分离、语音转录及大语言模型（LLMs），生成可解释的执法互动结构化摘要。采用定制评估流程，在高压力真实执法场景中测试转录质量与行为识别准确率。方法、计算技术与发现为执法审查、培训优化和问责机制提供实用路径，推动复杂执法数据知识发现的前沿进展。
原文摘要 · Abstract (English)
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.
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