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New access services in HbbTV based on a deep learning approach for media content analysis

Published online by Cambridge University Press:  04 December 2019

Silvia Uribe ,
Alberto Belmonte ,
Francisco Moreno ,
Álvaro Llorente ,
Juan Pedro López Open the ORCID record for Juan Pedro López [Opens in a new window]  and
Federico Álvarez
Silvia Uribe*
Affiliation:
Grupo de Aplicación de Telecomunicaciones Visuales, ETSIT, Universidad Politécnica de Madrid, Madrid, Spain
Alberto Belmonte
Affiliation:
Grupo de Aplicación de Telecomunicaciones Visuales, ETSIT, Universidad Politécnica de Madrid, Madrid, Spain
Francisco Moreno
Affiliation:
Grupo de Aplicación de Telecomunicaciones Visuales, ETSIT, Universidad Politécnica de Madrid, Madrid, Spain
Álvaro Llorente
Affiliation:
Grupo de Aplicación de Telecomunicaciones Visuales, ETSIT, Universidad Politécnica de Madrid, Madrid, Spain
Juan Pedro López
Affiliation:
Grupo de Aplicación de Telecomunicaciones Visuales, ETSIT, Universidad Politécnica de Madrid, Madrid, Spain
Federico Álvarez
Affiliation:
Grupo de Aplicación de Telecomunicaciones Visuales, ETSIT, Universidad Politécnica de Madrid, Madrid, Spain
*
Author for correspondence: Silvia Uribe, E-mail: sum@gatv.ssr.upm.es
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Abstract

Universal access on equal terms to audiovisual content is a key point for the full inclusion of people with disabilities in activities of daily life. As a real challenge for the current Information Society, it has been detected but not achieved in an efficient way, due to the fact that current access solutions are mainly based in the traditional television standard and other not automated high-cost solutions. The arrival of new technologies within the hybrid television environment together with the application of different artificial intelligence techniques over the content will assure the deployment of innovative solutions for enhancing the user experience for all. In this paper, a set of different tools for image enhancement based on the combination between deep learning and computer vision algorithms will be presented. These tools will provide automatic descriptive information of the media content based on face detection for magnification and character identification. The fusion of this information will be finally used to provide a customizable description of the visual information with the aim of improving the accessibility level of the content, allowing an efficient and reduced cost solution for all.

Keywords

Computer visiondeep learningface detectionmedia accessibility
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 4: Intelligent Interaction Design , November 2019 , pp. 399 - 415
Copyright
Copyright © Cambridge University Press 2019

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