AutoVAPS

Abstract

With the rapid development of Internet-of-Things, sensors and devices are connected enabling a variety of applications. One of the most attractive applications is Video Analysis for Public Safety (VAPS), which has got massive attention from both research community and industry. However, there are still challenges in the system design and implementation of the VAPS service, especially in the mobile environment. For example, law enforcement officers are equipped with body-worn camera when they are on duty, how to connect body-worn cameras with the law enforcement vehicle and enable the law enforcement vehicle to perform near real-time video analysis for the officer are still open questions. Inspired by the promising edge computing technology, we propose an IoT-Enabled public safety service called AutoVAPS which integrates body-worn cameras and other sensors on the vehicle for public safety. In AutoVAPS, we propose a reference architecture that consists of the data layer for data management, the model layer for edge intelligence, and the access layer for privacy-preserving data sharing and access. Object detection is implemented as a case study of AutoVAPS. Early evaluation illustrated the applicability and challenges of AutoVAPS.