# Vision-based Wearable Steering Assistance for People with Impaired Vision in Jogging

arXiv: https://arxiv.org/abs/2408.00332 · HTML: https://mlsanity.com/paper/2408.00332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks](https://mlsanity.com/paper/1708.04485/) (2017)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [Baidu Apollo EM Motion Planner](https://mlsanity.com/paper/1807.08048/) (2018)
- [YOLOP: You Only Look Once for Panoptic Driving Perception](https://mlsanity.com/paper/2108.11250/) (2021)
- [RESA: Recurrent Feature-Shift Aggregator for Lane Detection](https://mlsanity.com/paper/2008.13719/) (2020)
- [YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception](https://mlsanity.com/paper/2208.11434/) (2022)
- [Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques](https://mlsanity.com/paper/2002.06589/) (2020)

## Built on by (most cited first, 1 of 1)

- [Guide-LLM: An Embodied LLM Agent and Text-Based Topological Map for Robotic Guidance of People with Visual Impairments](https://mlsanity.com/paper/2410.20666/) (2024)

## Abstract (arXiv)

Outdoor sports pose a challenge for people with impaired vision. The demand for higher-speed mobility inspired us to develop a vision-based wearable steering assistance. To ensure broad applicability, we focused on a representative sports environment, the athletics track. Our efforts centered on improving the speed and accuracy of perception, enhancing planning adaptability for the real world, and providing swift and safe assistance for people with impaired vision. In perception, we engineered a lightweight multitask network capable of simultaneously detecting track lines and obstacles. Additionally, due to the limitations of existing datasets for supporting multi-task detection in athletics tracks, we diligently collected and annotated a new dataset (MAT) containing 1000 images. In planning, we integrated the methods of sampling and spline curves, addressing the planning challenges of curves. Meanwhile, we utilized the positions of the track lines and obstacles as constraints to guide people with impaired vision safely along the current track. Our system is deployed on an embedded device, Jetson Orin NX. Through outdoor experiments, it demonstrated adaptability in different sports scenarios, assisting users in achieving free movement of 400-meter at an average speed of 1.34 m/s, meeting the level of normal people in jogging. Our MAT dataset is publicly available from https://github.com/snoopy-l/MAT