A Dataset for Crucial Object Recognition in Blind and Low-Vision Individuals' Navigation

Autor:	Islam, Md Touhidul, Kabir, Imran, Pearce, Elena Ariel, Reza, Md Alimoor, Billah, Syed Masum
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.16777 Zobrazit plný text záznamu View this record from Arxiv