A Dataset for Crucial Object Recognition in Blind and Low-Vision Individuals' Navigation
It addresses the problem of inadequate object recognition for BLV individuals' navigation by providing a new dataset, which is incremental as it builds on existing computer vision methods.
This paper introduces a dataset of 21 videos and 90 crucial objects for blind and low-vision (BLV) navigation, revealing that current computer vision models perform poorly on these objects, highlighting the need for specialized datasets.
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.