# Low-latency hand gesture recognition with a low resolution thermal imager

arXiv: https://arxiv.org/abs/2004.11623 · HTML: https://mlsanity.com/paper/2004.11623/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Resource-Efficient Gesture Recognition using Low-Resolution Thermal Camera via Spiking Neural Networks and Sparse Segmentation](https://mlsanity.com/paper/2401.06563/) (2024)
- [A Deep Learning-based Multimodal Depth-Aware Dynamic Hand Gesture Recognition System](https://mlsanity.com/paper/2107.02543/) (2021)
- [Boosting Gesture Recognition with an Automatic Gesture Annotation Framework](https://mlsanity.com/paper/2401.11150/) (2024)
- [Simultaneous prediction of hand gestures, handedness, and hand keypoints using thermal images](https://mlsanity.com/paper/2303.01547/) (2023)
- [Identifying Human Indoor Daily Life Behavior employing Thermal Sensor Arrays (TSAs)](https://mlsanity.com/paper/2409.08508/) (2024)
- [Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices](https://mlsanity.com/paper/2605.13462/) (2026)

## Abstract (arXiv)

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 32x24 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 light-weight 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.