mlsanityData to 14 Oct 2026

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

arXiv 2407.16777 ↗First posted 23 Jul 2024cs.CV cs.HCcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 76 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

34 of 76 references.

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024WalkVLM:Aid Visually Impaired People Walking by Vision Language Modelcited by 13
  2. 2024Identifying Crucial Objects in Blind and Low-Vision Individuals' Navigationcited by 6
  3. 2025IKIWISI: An Interactive Visual Pattern Generator for Evaluating the Reliability of Vision-Language Models Without Ground Truthcited by 0
  4. 2025Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Informationcited by 0
  5. 2026NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigationcited by 0