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

Video

DOI

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.