Resource-Efficient Gesture Recognition using Low-Resolution Thermal
  Camera via Spiking Neural Networks and Sparse Segmentation
Ali Safa Wout Mommen Lars Keuninckx
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.