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 costeffective 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
offering compatibility with both GPU and CPU environments.
