Sentinel: Smart Surveillance System with Automated Anomaly Detection

Abstract

This project addresses the critical need for efficient and real-time anomaly detection in surveillance systems, considering the widespread deployment of surveillance cameras in diverse environments. Traditional server-based approaches pose challenges related to cost, network strain, and responsiveness. Leveraging edge computing and deep learning, our project aims to develop a cost- effective solution. Success criteria involve achieving state-of-the-art accuracy, real-time inference and streaming, and seamless integration with existing networks. Our goal is to explore existing solutions and propose an architecture that optimizes model performance on edge devices while