Real-Time Sign Language Detection using Human Pose Estimation
share
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 Corpus. Using a recurrent model directly on the input, we see improvements of up to 91 application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.
READ FULL TEXT