| Title: | OS17-4 Classification of Human Activity by Spiking Neural Networks using Event-based Vision Sensors |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 484-486 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS17-4 |
| Author(s): | Aryan Rakheja, Rohan Saini, Ryuta Toyoda, Yuichiro Tanaka, Hakaru Tamukoh |
| Publication Date: | February 13, 2025 |
| Keywords: | Spiking neural network (SNN), Event-based vision sensor (EVS) |
| 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. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS17/OS17-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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