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.

翻译：本文提出了一种新颖的手势识别方法，采用低成本、低分辨率（24×32）热传感器采集数据，通过脉冲神经网络（SNN）处理后，结合稀疏分割与基于鲁棒主成分分析（R-PCA）的特征手势分类。相较于传统RGB摄像头，本系统对光照变化不敏感，且成本显著低于文献中此前使用的高频雷达、飞行时间摄像头及高分辨率热传感器。关键创新在于，本文首次采用近期提出的单稳态多谐振荡器（MMV）神经网络作为新型SNN架构，其内存与计算复杂度比深度学习方法低一个数量级以上，同时基于汽车座舱场景采集的5类热相机数据集，取得了93.9%的最高手势识别准确率。我们公开了该数据集以促进后续研究。