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

• Published in: AI EDAM Vol. 33; no. 4; pp. 399 - 415
• Main Authors: Uribe, Silvia, Belmonte, Alberto, Moreno, Francisco, Llorente, Álvaro, López, Juan Pedro, Álvarez, Federico
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 01.11.2019
• Subjects: Algorithms; Artificial intelligence; Automation; Computer vision; Consumer behavior; Consumers; Consumption; Content analysis; Deafness; Deep learning; Face recognition; Handicapped accessibility; Image enhancement; Internet; IPTV; Machine learning; Multimedia; New technology; People with disabilities; Television programming
• ISSN: 0890-0604; 1469-1760
• DOI: 10.1017/S0890060419000350

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.