  This paper addresses the daily challenges encountered by visually impaired
individuals, such as limited access to information, navigation difficulties,
and barriers to social interaction. To alleviate these challenges, we introduce
a novel visual question answering dataset. Our dataset offers two significant
advancements over previous datasets: Firstly, it features videos captured using
a 360-degree egocentric wearable camera, enabling observation of the entire
surroundings, departing from the static image-centric nature of prior datasets.
Secondly, unlike datasets centered on singular challenges, ours addresses
multiple real-life obstacles simultaneously through an innovative
visual-question answering framework. We validate our dataset using various
state-of-the-art VideoQA methods and diverse metrics. Results indicate that
while progress has been made, satisfactory performance levels for AI-powered
assistive services remain elusive for visually impaired individuals.
Additionally, our evaluation highlights the distinctive features of the
proposed dataset, featuring ego-motion in videos captured via 360-degree
cameras across varied scenarios.
