Low-latency hand gesture recognition with a low resolution thermal imager
This work addresses the need for affordable gesture recognition systems in applications like automotive controls, though it is incremental in using existing methods on new data.
The paper tackled the problem of enabling hand gesture recognition with a low-cost thermal camera and processor, achieving 95.9% classification accuracy and 83% mAP detection accuracy with a latency of one frame.
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.