Towards Human Action Recognition in Open Environments: A Proof of Concept on Spanish Sign Language Vowels
Article No.: 16, Pages 1 - 4
Abstract
Gestures and body language are fundamental communication mechanisms between human beings. Trying to make machines recognize this language is an active area of research in the field of computer vision that has applications in fields as diverse as video surveillance and healthcare. Usually, most of these developments are carried out in closed and controlled environments (e.g., a laboratory with specific conditions of light, climate, furniture, backgrounds, and so forth). Real-time motion and gesture recognition through cameras in an uncontrolled environment, open environments, or outdoor environments (such as a shopping mall, museum lobby, train station platform, or airport terminal) and allowing interaction with passers-by in these spaces is not so much studied. This task presents challenges related to the technological, social, and legal factors that need to be carefully addressed. This work is a first step towards human action recognition in open environments adapted to interaction without physical contact using videos captured with a camera in real-time in these spaces. In this manner, as a proof of concept we explore the development of a system that allows real-time recognition of Spanish Sign Language (Lengua de Signos Española - LSE), in particular, we select the vowels as gestures.
References
[1]
Md Atiqur Rahman Ahad, Upal Mahbub, and Tauhidur Rahman. 2021. Contactless human activity analysis. Vol. 200. Springer.
[2]
Gary Bradski. 2000. The openCV library.Dr. Dobb’s Journal: Software Tools for the Professional Programmer 25, 11 (2000), 120–123.
[3]
Ángela Casado-García, César Domínguez, Manuel García-Domínguez, Jónathan Heras, Adrián Inés, Eloy Mata, and Vico Pascual. 2019. CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks. BMC Bioinformatics 20, 1 (2019), 1–14.
[4]
Amanda Duarte, Shruti Palaskar, Lucas Ventura, Deepti Ghadiyaram, Kenneth DeHaan, Florian Metze, Jordi Torres, and Xavier Giro-i Nieto. 2021. How2sign: a large-scale multimodal dataset for continuous american sign language. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2735–2744.
[5]
Jeremy Howard and Sylvain Gugger. 2020. Deep Learning for Coders with fastai and PyTorch. O’Reilly Media.
[6]
Jian Liu, Naveed Akhtar, and Ajmal Mian. 2019. Skepxels: Spatio-temporal image representation of human skeleton joints for action recognition. In CVPR workshops. 10–19.
[7]
Tahmida Mahmud and Mahmudul Hasan. 2021. Vision-Based Human Activity Recognition. Contactless Human Activity Analysis (2021), 1–42.
[8]
Tom O’Malley, Elie Bursztein, James Long, François Chollet, Haifeng Jin, Luca Invernizzi, 2019. KerasTuner. https://github.com/keras-team/keras-tuner.
[9]
Tansel Özyer, Duygu Selin Ak, and Reda Alhajj. 2021. Human action recognition approaches with video datasets—A survey. Knowledge-Based Systems 222 (2021), 106995.
[10]
Refat Khan Pathan, Munmun Biswas, Suraiya Yasmin, Mayeen Uddin Khandaker, Mohammad Salman, and Ahmed AF Youssef. 2023. Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network. Scientific Reports 13, 1 (2023), 16975.
[11]
Konstantinos Poulinakis. [n. d.]. Complete Practical Tutorial on Keras Tuner GitHub. https://github.com/Poulinakis-Konstantinos/Blogging-Journey/blob/main/Keras-Tuner-Complete-Tutorial/keras-tuner.ipynb. Accessed: 2023-04-25.
[12]
Adrián Sánchez-Caballero, David Fuentes-Jiménez, and Cristina Losada-Gutiérrez. 2022. Real-time human action recognition using raw depth video-based recurrent neural networks. Multimedia Tools and Applications (2022), 1–23.
[13]
Arya Sarkar, Avinandan Banerjee, Pawan Kumar Singh, and Ram Sarkar. 2022. 3D Human Action Recognition: Through the eyes of researchers. Expert Systems with Applications (2022), 116424.
[14]
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. 2014. CNN features off-the-shelf: an astounding baseline for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 806–813.
[15]
Aditya Sharma. 2023. Training the YOLOv8 Object Detector for OAK-D. https://pyimg.co/9qceip. Accessed: 2023-05-16.
[16]
Anwaar Ulhaq, Naveed Akhtar, Ganna Pogrebna, and Ajmal Mian. 2022. Vision transformers for action recognition: A survey. arXiv preprint arXiv:2209.05700 (2022).
[17]
Fan Zhang, Valentin Bazarevsky, Andrey Vakunov, Andrei Tkachenka, George Sung, Chuo-Ling Chang, and Matthias Grundmann. 2020. Mediapipe hands: On-device real-time hand tracking. arXiv preprint arXiv:2006.10214 (2020).
Index Terms
- Towards Human Action Recognition in Open Environments: A Proof of Concept on Spanish Sign Language Vowels
Recommendations
Feature design scheme for Kinect-based DTW human gesture recognition
Feature selection is a crucial factor in Kinect-based pattern recognition, including common human gesture recognition. For Kinect-based human gesture recognition, the information contained in the feature extracted for gesture recognition is ...
A rule-based translation from written Spanish to Spanish Sign Language glosses
HighlightsRule-based approach for a Spanish-to-LSE machine translation system for wide-domain application.The proposed approach is based on the transfer at the level of syntatic functions from dependency analysis and employs three different stages: ...
Deep learning and RGB-D based human action, human–human and human–object interaction recognition: A survey
AbstractHuman activity recognition is one of the most studied topics in the field of computer vision. In recent years, with the availability of RGB-D sensors and powerful deep learning techniques, research on human activity recognition has ...
Highlights- A comprehensive categorical survey of RGB-D human action/interaction recognition.
Comments
Information & Contributors
Information
Published In
June 2024
155 pages
ISBN:9798400717871
DOI:10.1145/3657242
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 June 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
- Comunidad Autónoma de La Rioja
- MCIN/AEI/ 10.13039/501100011033
Conference
INTERACCION 2024
Acceptance Rates
Overall Acceptance Rate 109 of 163 submissions, 67%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 9Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)4
Reflects downloads up to 09 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format