Classification of Human Activity by Event-based Vision Sensors using Echo State Networks

We propose a system for human activity recognition using an event-based vision sensor (EVS) with echo state networks (ESNs).Conventional cameras are susceptible to motion blur and require computationally intensive methods, whereas EVS provides no motion blur and low latency.Our research aims to enable accurate recognition of human activities by using energy-efficient methods.Therefore, we adopt ESNs, which require low computational costs, for the classifier.Additionally, we use feature extraction algorithms such as optical flow and histogram of gradients to improve accuracy.We used an EVS activity recognition dataset created by us containing six human activities and 600 videos.The results showed that our hybrid approach outperformed several techniques.We achieved 89% accuracy when trained with ridge regression.