Classification of Human Activity by Spiking Neural Networks using Event-based Vision Sensors

This paper presents a human action classification system using a spiking neural network (SNN) and an event-based vision sensor (EVS).The EVS captures asynchronous data with high temporal resolution, wide dynamic range, and motion blur resistance.SNNs, inspired by biological neurons, process this data event-driven, ensuring energy efficiency, low latency, and scalability.Using a custom EVS dataset of 600 videos across four action types and the optical flow for feature extraction, the system achieved 93% accuracy, offering an efficient solution for action recognition.