Hand gesture detection is a well-explored area in computer vision with
applications in various forms of Human-Computer Interactions. In this work, we
propose a technique for simultaneous hand gesture classification, handedness
detection, and hand keypoints localization using thermal data captured by an
infrared camera. Our method uses a novel deep multi-task learning architecture
that includes shared encoderdecoder layers followed by three branches dedicated
for each mentioned task. We performed extensive experimental validation of our
model on an in-house dataset consisting of 24 users data. The results confirm
higher than 98 percent accuracy for gesture classification, handedness
detection, and fingertips localization, and more than 91 percent accuracy for
wrist points localization.

本文提出了一种使用红外热成像技术进行的手势分类、左右手检测、手部关键点定位的深度多任务学习模型，通过在包含 24 个用户数据的自有数据集上进行实验验证，结果表明手势分类、左右手检测、指尖定位的准确率均高于 98％，手腕定位的准确率超过了 91％。

利用热像检测同时预测手势、惯用手及关键点

Simultaneous prediction of hand gestures, handedness, and hand keypoints using thermal images

Mobile virtual reality (VR) head mounted displays (HMD) have become popular
among consumers in recent years. In this work, we demonstrate real-time
egocentric hand gesture detection and localization on mobile HMDs. Our main
contributions are: 1) A novel mixed-reality data collection tool to automatic
annotate bounding boxes and gesture labels; 2) The largest-to-date egocentric
hand gesture and bounding box dataset with more than 400,000 annotated frames;
3) A neural network that runs real time on modern mobile CPUs, and achieves
higher than 76% precision on gesture recognition across 8 classes.

本文演示了在移动 VR 头戴式显示器上实时实现自我中心手势检测和定位的方法。通过创新的混合现实数据收集工具，以及超过 400,000 帧的最大自我中心手势和边界框数据集，在现代移动 CPU 上实现的神经网络可以在 8 个类别中实现超过 76％的手势识别精度。