This work proposes a novel approach for hand gesture recognition using an
inexpensive, low-resolution (24 x 32) thermal sensor processed by a Spiking
Neural Network (SNN) followed by Sparse Segmentation and feature-based gesture
classification via Robust Principal Component Analysis (R-PCA). Compared to the
use of standard RGB cameras, the proposed system is insensitive to lighting
variations while being significantly less expensive compared to high-frequency
radars, time-of-flight cameras and high-resolution thermal sensors previously
used in literature. Crucially, this paper shows that the innovative use of the
recently proposed Monostable Multivibrator (MMV) neural networks as a new class
of SNN achieves more than one order of magnitude smaller memory and compute
complexity compared to deep learning approaches, while reaching a top gesture
recognition accuracy of 93.9% using a 5-class thermal camera dataset acquired
in a car cabin, within an automotive context. Our dataset is released for
helping future research.

使用低分辨率 (24 x 32) 热传感器，结合脉冲神经网络、稀疏分割和基于特征的手势分类，提出了一种新颖的手势识别方法。与标准 RGB 摄像头相比，该系统对光照变化不敏感，且价格显著低于以往文献中使用的高频雷达、飞行时间摄像头和高分辨率热传感器。关键是，本文通过创新地使用最近提出的单稳态多谐振荡器 (MMV) 神经网络作为新型脉冲神经网络，在记忆和计算复杂度方面较深度学习方法小一个数量级，同时在汽车环境下使用 5 类热相机数据集达到 93.9% 的手势识别准确率。我们提供了该数据集以供未来研究使用。