# Leveraging Structure from Motion to Localize Inaccessible Bus Stops

arXiv: https://arxiv.org/abs/2210.03646 · HTML: https://mlsanity.com/paper/2210.03646/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks](https://mlsanity.com/paper/1709.08429/) (2017)
- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [Meta-Sim: Learning to Generate Synthetic Datasets](https://mlsanity.com/paper/1904.11621/) (2019)
- [Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques](https://mlsanity.com/paper/2006.16471/) (2020)
- [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021)
- [SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments](https://mlsanity.com/paper/2012.08939/) (2020)
- [Vehicle Localization and Control on Roads with Prior Grade Map](https://mlsanity.com/paper/1809.04167/) (2018)

## Abstract (arXiv)

The detection of hazardous conditions near public transit stations is necessary for ensuring the safety and accessibility of public transit. Smart city infrastructures aim to facilitate this task among many others through the use of computer vision. However, most state-of-the-art computer vision models require thousands of images in order to perform accurate detection, and there exist few images of hazardous conditions as they are generally rare. In this paper, we examine the detection of snow-covered sidewalks along bus routes. Previous work has focused on detecting other vehicles in heavy snowfall or simply detecting the presence of snow. However, our application has an added complication of determining if the snow covers areas of importance and can cause falls or other accidents (e.g. snow covering a sidewalk) or simply covers some background area (e.g. snow on a neighboring field). This problem involves localizing the positions of the areas of importance when they are not necessarily visible. We introduce a method that utilizes Structure from Motion (SfM) rather than additional annotated data to address this issue. Specifically, our method learns the locations of sidewalks in a given scene by applying a segmentation model and SfM to images from bus cameras during clear weather. Then, we use the learned locations to detect if and where the sidewalks become obscured with snow. After evaluating across various threshold parameters, we identify an optimal range at which our method consistently classifies different categories of sidewalk images correctly. Although we demonstrate an application for snow coverage along bus routes, this method can extend to other hazardous conditions as well. Code for this project is available at https://github.com/ind1010/SfM_for_BusEdge.