# Unsupervised Domain Adaptation Learning Algorithm for RGB-D Staircase Recognition

arXiv: https://arxiv.org/abs/1903.01212 · HTML: https://mlsanity.com/paper/1903.01212/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Multimodal Across Domains Gaze Target Detection](https://mlsanity.com/paper/2208.10822/) (2022)
- [DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation](https://mlsanity.com/paper/2103.12768/) (2021)
- [Multi-Modal RGB-D Scene Recognition Across Domains](https://mlsanity.com/paper/2103.14672/) (2021)
- [RGB-D-based Stair Detection using Deep Learning for Autonomous Stair Climbing](https://mlsanity.com/paper/2212.01098/) (2022)

## Abstract (arXiv)

Detection and recognition of staircase as upstairs, downstairs and negative (e.g., ladder) are the fundamental of assisting the visually impaired to travel independently in unfamiliar environments. Previous researches have focused on using massive amounts of RGB-D scene data to train traditional machine learning (ML) based models to detect and recognize the staircase. However, the performance of traditional ML techniques is limited by the amount of labeled RGB-D staircase data. In this paper, we apply an unsupervised domain adaptation approach in deep architectures to transfer knowledge learned from the labeled RGB-D stationary staircase dataset to the unlabeled RGB-D escalator dataset. By utilizing the domain adaptation method, our feedforward convolutional neural networks (CNN) based feature extractor with 5 convolution layers can achieve 100% classification accuracy on testing the labeled stationary staircase data and 80.6% classification accuracy on testing the unlabeled escalator data. We demonstrate the success of the approach for classifying staircase on two domains with a limited amount of data. To further demonstrate the effectiveness of the approach, we also validate the same CNN model without domain adaptation and compare its results with those of our proposed architecture.