# Identifying Crucial Objects in Blind and Low-Vision Individuals' Navigation

arXiv: https://arxiv.org/abs/2408.13175 · HTML: https://mlsanity.com/paper/2408.13175/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Recognize Anything: A Strong Image Tagging Model](https://mlsanity.com/paper/2306.03514/) (2023)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://mlsanity.com/paper/1708.01566/) (2017)
- [Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models](https://mlsanity.com/paper/2106.00245/) (2021)
- [LAVIS: A Library for Language-Vision Intelligence](https://mlsanity.com/paper/2209.09019/) (2022)
- [A Dataset for Crucial Object Recognition in Blind and Low-Vision Individuals' Navigation](https://mlsanity.com/paper/2407.16777/) (2024)

## Built on by (most cited first, 6 of 6)

- [WalkVLM:Aid Visually Impaired People Walking by Vision Language Model](https://mlsanity.com/paper/2412.20903/) (2024)
- [Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users](https://mlsanity.com/paper/2503.22610/) (2025)
- [Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users](https://mlsanity.com/paper/2602.16930/) (2026)
- [IKIWISI: An Interactive Visual Pattern Generator for Evaluating the Reliability of Vision-Language Models Without Ground Truth](https://mlsanity.com/paper/2505.22305/) (2025)
- [Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information](https://mlsanity.com/paper/2512.10102/) (2025)
- [NavAble: A Large-Scale Dataset and Synthetic Data Generation Pipeline for Blind Navigation](https://mlsanity.com/paper/2610.19330/) (2026)

## Abstract (arXiv)

This paper presents a curated list of 90 objects essential for the navigation of blind and low-vision (BLV) individuals, encompassing road, sidewalk, and indoor environments. We develop the initial list by analyzing 21 publicly available videos featuring BLV individuals navigating various settings. Then, we refine the list through feedback from a focus group study involving blind, low-vision, and sighted companions of BLV individuals. A subsequent analysis reveals that most contemporary datasets used to train recent computer vision models contain only a small subset of the objects in our proposed list. Furthermore, we provide detailed object labeling for these 90 objects across 31 video segments derived from the original 21 videos. Finally, we make the object list, the 21 videos, and object labeling in the 31 video segments publicly available. This paper aims to fill the existing gap and foster the development of more inclusive and effective navigation aids for the BLV community.