Nath Tan Nguyen

Bio: Nath Tan Nguyen is an academic researcher from Laval University. The author has contributed to research in topics: Video tracking & Rendering (computer graphics). The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

Towards computer-vision software tools to increase production and accessibility of video description for people with vision loss

Abstract: This paper presents the status of a R&D project targeting the development of computer-vision tools to assist humans in generating and rendering video description for people with vision loss. Three principal issues are discussed: (1) production practices, (2) needs of people with vision loss, and (3) current system design, core technologies and implementation. The paper provides the main conclusions of consultations with producers of video description regarding their practices and with end-users regarding their needs, as well as an analysis of described productions that lead to propose a video description typology. The current status of a prototype software is also presented (audio-vision manager) that uses many computer-vision technologies (shot transition detection, key-frame identification, key-face recognition, key-text spotting, visual motion, gait/gesture characterization, key-place identification, key-object spotting and image categorization) to automatically extract visual content, associate textual descriptions and add them to the audio track with a synthetic voice. A proof of concept is also briefly described for a first adaptive video description player which allows end users to select various levels of video description.

Annotation-based video enrichment for blind people: a pilot study on the use of earcons and speech synthesis

Abstract: Our approach to address the question of online video accessibility for people with sensory disabilities is based on video annotations that are rendered as video enrichments during the playing of the video. We present an exploratory work that focuses on video accessibility for blind people with audio enrichments composed of speech synthesis and earcons (i.e. nonverbal audio messages). Our main results are that earcons can be used together with speech synthesis to enhance understanding of videos; that earcons should be accompanied with explanations; and that a potential side effect of earcons is related to video rhythm perception.

Human-in-the-Loop Machine Learning to Increase Video Accessibility for Visually Impaired and Blind Users

Abstract: Video accessibility is crucial for blind and visually impaired individuals for education, employment, and entertainment purposes. However, professional video descriptions are costly and time-consuming. Volunteer-created video descriptions could be a promising alternative, however, they can vary in quality and can be intimidating for novice describers. We developed a Human-in-the-Loop Machine Learning (HILML) approach to video description by automating video text generation and scene segmentation and allowing humans to edit the output. The HILML approach facilitates human-machine collaboration to produce high quality video descriptions while keeping a low barrier to entry for volunteer describers. Our HILML system was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance. The quality of the video descriptions and understanding of the topic created by the HILML system compared to the human-only condition were rated as being significantly higher by blind and visually impaired users.

A computer-vision-assisted system for Videodescription scripting

Abstract: We present an application of video indexing/summarization to produce Videodescription (VD) for the blinds. Audio and computer vision technologies can automatically detect and recognize many elements that are pertinent to VD which can speed-up the VD production process. We have developed and integrated many of them into a first computer-assisted VD production software. The paper presents the main outcomes of this R&D activity started 5 years ago in our laboratory. Up to now, usability performance on various video and TV series types have shown a reduction of up to 50% in the VD time production process.

What Makes Videos Accessible to Blind and Visually Impaired People

Abstract: User-generated videos are an increasingly important source of information online, yet most online videos are inaccessible to blind and visually impaired (BVI) people. To find videos that are accessible, or understandable without additional description of the visual content, BVI people in our formative studies reported that they used a time-consuming trial-and-error approach: clicking on a video, watching a portion, leaving the video, and repeating the process. BVI people also reported video accessibility heuristics that characterize accessible and inaccessible videos. We instantiate 7 of the identified heuristics (2 audio-related, 2 video-related, and 3 audio-visual) as automated metrics to assess video accessibility. We collected a dataset of accessibility ratings of videos by BVI people and found that our automatic video accessibility metrics correlated with the accessibility ratings (Adjusted R2 = 0.642). We augmented a video search interface with our video accessibility metrics and predictions. BVI people using our augmented video search interface selected an accessible video more efficiently than when using the original search interface. By integrating video accessibility metrics, video hosting platforms could help people surface accessible videos and encourage content creators to author more accessible products, improving video accessibility for all.

Increasing Video Accessibility for Visually Impaired Users with Human-in-the-Loop Machine Learning

Abstract: Video accessibility is crucial for blind and visually impaired individuals for education, employment, and entertainment purposes. However, professional video descriptions are costly and time-consuming. Volunteer-created video descriptions could be a promising alternative, however, they can vary in quality and can be intimidating for novice describers. We developed a Human-in-the-Loop Machine Learning (HILML) approach to video description by automating video text generation and scene segmentation while allowing humans to edit the output. Our HILML system was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance. The quality of the video descriptions and understanding of the topic created by the HILML system compared to the human-only condition were rated as being significantly higher by blind and visually impaired users.