Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification

Hussein Chaaban, Michèle Gouiffès, Annelies Braffort

2021

Abstract

The automatic recognition of Sign Languages is the main focus of most of the works in the field, which explains the progressing demand on the annotated data to train the dedicated models. In this paper, we present a semi automatic annotation system for Sign Languages. Such automation will not only help to create training data but it will reduce as well the processing time and the subjectivity of manual annotations done by linguists in order to study the sign language. The system analyses hand shapes, hands speed variations, and face landmarks to annotate base level features and to separate the different signs. In a second stage, signs are classified into two types, whether they are lexical (i.e. present in a dictionary) or iconic (illustrative), using a probabilistic model. The results show that our system is partially capable of annotating automatically the video sequence with a F1 score = 0.68 for lexical sign annotation and an error of 3.8 frames for sign segmentation. An expert validation of the annotations is still needed.

Paper Citation

in Harvard Style

Chaaban H., Gouiffès M. and Braffort A. (2021). Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 484-491. DOI: 10.5220/0010247104840491

in Bibtex Style

@conference{visapp21,
author={Hussein Chaaban and Michèle Gouiffès and Annelies Braffort},
title={Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={484-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010247104840491},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification
SN - 978-989-758-488-6
AU - Chaaban H.
AU - Gouiffès M.
AU - Braffort A.
PY - 2021
SP - 484
EP - 491
DO - 10.5220/0010247104840491
PB - SciTePress