Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.os17-2

Authors: Rohan Saini, Aryan Rakheja, Ryuta Toyoda, Yuichiro Tanaka, Hakaru Tamukoh

Institution: Kyushu Institute of Technology

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze