Thermal imaging gesture recognition method based on binary neural network

Jinlei He; Junlei Liu; Hanwen Ou; Liangliang Tian

doi:10.1117/12.3012482

21 December 2023 Thermal imaging gesture recognition method based on binary neural network

Jinlei He, Junlei Liu, Hanwen Ou, Liangliang Tian

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701E (2023) https://doi.org/10.1117/12.3012482
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Due to the complexity of environmental factors, such as background and lighting, traditional vision-based gesturerecognition algorithms tend to be less robust. Similarly, gesture recognition algorithms based on neural networks havethe drawbacks of large computational requirements and high storage parameters, thereby hindering effective deployment. This paper proposes a novel gesture recognition algorithm based on infrared thermal imaging sensor, which effectivelyaddresses the above challenges. By collecting thermal imaging gesture data and performing preprocessing, an improvedbinary neural network is trained and deployed on a microprocessor device. Results show that the proposed binaryneural network achieves a recognition rate of 99% for three gestures, and 95% for five gestures. Compared with convolutional neural networks, which have higher computational requirements and more model storage parameters, the proposedalgorithm achieves a frame rate of 44FPS on STM32F4 microprocessor and 366FPS on H7, highlighting its potential asan effective gesture recognition technology using thermal imaging sensors.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinlei He, Junlei Liu, Hanwen Ou, and Liangliang Tian "Thermal imaging gesture recognition method based on binary neural network", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701E (21 December 2023); https://doi.org/10.1117/12.3012482

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