Abstract
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 Public DGS Corpus. Using a recurrent model directly on the input, we see improvements of up to 91% accuracy, while still working under 4 ms. We describe a demo application to sign language detection in the browser in order to demonstrate its usage possibility in videoconferencing applications.
Original language | English |
---|---|
Title of host publication | Computer Vision – ECCV 2020 Workshops, Proceedings |
Editors | Adrien Bartoli, Andrea Fusiello |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 237-248 |
Number of pages | 12 |
ISBN (Print) | 9783030660956 |
DOIs | |
State | Published - 2020 |
Event | Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 12536 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 |
---|---|
Country/Territory | United Kingdom |
City | Glasgow |
Period | 23/08/20 → 28/08/20 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- Sign language detection
- Sign language processing