Neuromorphic Energy Efficient Motion Detection System Using SNN

This research paper presents an energy-efficient motion detection system that integrates Spiking Neural Networks (SNNs) with YOLO-based detection and facial recognition. Unlike traditional models that continuously process every frame, our method follows an event-driven approach. The SNN spikes only when motion occurs. Our system does not process every frame; it only activates when there is a change in frame. This saves energy, and it sends quick alerts whenever an unfamiliar face is detected, balancing accuracy with efficiency by using deep learning together with neuromorphic methods, making it a strong build for use in smart surveillance and edge-level security.