Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation
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| Format: | Preprint |
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2024
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| _version_ | 1866909071179776000 |
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| author | Safa, Ali Mommen, Wout Keuninckx, Lars |
| author_facet | Safa, Ali Mommen, Wout Keuninckx, Lars |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_06563 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation Safa, Ali Mommen, Wout Keuninckx, Lars Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2401.06563 |