  This paper introduces a dataset for improving real-time object recognition
systems to aid blind and low-vision (BLV) individuals in navigation tasks. The
dataset comprises 21 videos of BLV individuals navigating outdoor spaces, and a
taxonomy of 90 objects crucial for BLV navigation, refined through a focus
group study. We also provide object labeling for the 90 objects across 31 video
segments created from the 21 videos. A deeper analysis reveals that most
contemporary datasets used in training computer vision models contain only a
small subset of the taxonomy in our dataset. Preliminary evaluation of
state-of-the-art computer vision models on our dataset highlights shortcomings
in accurately detecting key objects relevant to BLV navigation, emphasizing the
need for specialized datasets. We make our dataset publicly available, offering
valuable resources for developing more inclusive navigation systems for BLV
individuals.
