Implementación de tecnologías de borde y técnicas de Deep Learning para vigilancia en áreas remotas con canales de comunicación limitados

Abstract

The need to safeguard people, spaces, and cities has been a key driver for the development of video surveillance systems based on increasingly robust and intelligent deep learning models. However, in contexts like Colombia, characterized by significant limitations in its national telecommunications infrastructure, this task faces various challenges. In response to this issue, the present work focuses on the research, testing, and practical evaluation of optimal technologies and techniques for implementing deep learning models on edge devices. To this end, guidelines derived from the current literature were established and applied, which were then assessed in a practical setting. The training of different deep learning models was carried out using a dataset of images specifically designed for the problem domain. Subsequently, the models were tested through the design and development of an edge system in a practical environment, and the results were compared with those from a currently active production system.