  Video accessibility is crucial for blind and low vision users for equitable
engagements in education, employment, and entertainment. Despite the
availability of professional and amateur services and tools, most
human-generated descriptions are expensive and time consuming. Moreover, the
rate of human-generated descriptions cannot match the speed of video
production. To overcome the increasing gaps in video accessibility, we
developed a hybrid system of two tools to 1) automatically generate
descriptions for videos and 2) provide answers or additional descriptions in
response to user queries on a video. Results from a mixed-methods study with 26
blind and low vision individuals show that our system significantly improved
user comprehension and enjoyment of selected videos when both tools were used
in tandem. In addition, participants reported no significant difference in
their ability to understand videos when presented with autogenerated
descriptions versus human-revised autogenerated descriptions. Our results
demonstrate user enthusiasm about the developed system and its promise for
providing customized access to videos. We discuss the limitations of the
current work and provide recommendations for the future development of
automated video description tools.

Human-Computer Interaction;Computer Vision and Pattern Recognition;Machine Learning