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This work proposes a novel approach for hand gesture recognition using aninexpensive, low-resolution (24 x 32) thermal sensor processed by a SpikingNeural Network (SNN) followed by Sparse Segmentation and feature-based gestureclassification via Robust Principal Component Analysis (R-PCA). Compared to theuse of standard RGB cameras, the proposed system is insensitive to lightingvariations while being significantly less expensive compared to high-frequencyradars, time-of-flight cameras and high-resolution thermal sensors previouslyused in literature. Crucially, this paper shows that the innovative use of therecently proposed Monostable Multivibrator (MMV) neural networks as a new classof SNN achieves more than one order of magnitude smaller memory and computecomplexity compared to deep learning approaches, while reaching a top gesturerecognition accuracy of 93.9% using a 5-class thermal camera dataset acquiredin a car cabin, within an automotive context. Our dataset is released forhelping future research.

Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation