Low-Latency Hand Gesture Recognition with a Low Resolution Thermal Imager
Abstract
Using hand gestures to answer a call or to control the radio while driving a car, is nowadays an established feature in more expensive cars. High resolution time-of-flight cameras and powerful embedded processors usually form the heart of these gesture recognition systems. This however comes with a price tag. We therefore investigate the possibility to design an algorithm that predicts hand gestures using a cheap low-resolution thermal camera with only 32×24 pixels, which is light-weight enough to run on a low-cost processor. We recorded a new dataset of over 1300 video clips for training and evaluation and propose a lightweight low-latency prediction algorithm. Our best model achieves 95.9% classification accuracy and 83% mAP detection accuracy while its processing pipeline has a latency of only one frame.
Cite
Text
Vandersteegen et al. "Low-Latency Hand Gesture Recognition with a Low Resolution Thermal Imager." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00057Markdown
[Vandersteegen et al. "Low-Latency Hand Gesture Recognition with a Low Resolution Thermal Imager." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/vandersteegen2020cvprw-lowlatency/) doi:10.1109/CVPRW50498.2020.00057BibTeX
@inproceedings{vandersteegen2020cvprw-lowlatency,
title = {{Low-Latency Hand Gesture Recognition with a Low Resolution Thermal Imager}},
author = {Vandersteegen, Maarten and Reusen, Wouter and Van Beeck, Kristof and Goedemé, Toon},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {440-449},
doi = {10.1109/CVPRW50498.2020.00057},
url = {https://mlanthology.org/cvprw/2020/vandersteegen2020cvprw-lowlatency/}
}