论文标题
使用人姿势估计的实时手语检测
Real-Time Sign Language Detection using Human Pose Estimation
论文作者
论文摘要
我们提出了一种轻巧的实时手语检测模型，因为我们确定了在视频会议中对这种情况的需求。我们根据人姿势估计提取光流特征，并使用线性分类器表明，这些特征具有80％的精度，并在DGS语料库上进行了评估。直接在输入上使用经常性模型，我们在4毫秒以下工作时，提高了高达91％的精度。我们描述了在浏览器中进行签名语言检测的演示应用程序，以证明其在视频会议应用程序中的使用可能性。
We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing. We extract optical flow features based on human pose estimation and, using a linear classifier, show these features are meaningful with an accuracy of 80%, evaluated on the DGS Corpus. Using a recurrent model directly on the input, we see improvements of up to 91% accuracy, while still working under 4ms. We describe a demo application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.
