Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.os17-4

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

Institution: Kyushu Institute of Technology

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Japan Society for the Promotion of Science, Advanced Low Carbon Technology Research and Development Program

Awards: G2364066242, G7712781798