Application for Detecting Child Abuse via Real-Time Video Surveillance

Abstract

Applications of real-time video surveillance are contributing to traffic management and public safety. One example is analyzing crowd behavior and responding immediately to violations, such as assault, fighting, and child abuse. However, monitoring real-time video surveillance continuously is arduous work, and there is a high chance of missed violations. This paper proposes an application for detecting child abuse using deep learning methods. The combination of real-time video surveillance and deep learning can contribute to avoiding child abuse and responding on time.