WatchOverGPT: A Framework for Real-Time Crime Detection and Response Using Wearable Camera and Large Language Model
Abdur R. Shahid, Syed Mhamudul Hasan, Malithi Wanniarachchi Kankanamge, Md Zarif Hossain, Ahmed Imteaj · 2024
In the era of Large Language Models (LLMs), the application of advanced AI technologies to data captured by wearables devices, combined with the fusion of contextual data, presents a revolutionary approach to enhancing real-time public safety, individual security, and emergency response. In this paper, we introduce WatchOverGPT, a novel framework that leverages this integration to promptly identify and respond to potential life-threatening criminal activities and safety concerns. WatchOverGPT combines the capabilities of wearable cameras, smartphones' location data, and LLM-based advanced con-versational AI communication through Generative Pre-trained Transformer (GPT). The core of this framework involves a wearable camera connected to the user's smartphone, which continuously captures and analyzes the environment for signs of distress or criminal behaviors, including human actions and the presence of weapons, coupled with location and other information from the smartphone by which GPT-based application provides an autonomous decision-making process. This paper explores the framework's design, implementation, and potential impact of LLM applications on public safety. The proposed framework aims to bridge the gap between safety threats and emergency response teams in the fight against crime through real-time data processing and AI -driven autonomous communication, enhancing the security of individuals in various settings,