  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.
