StairNet: A Computer Vision Dataset for Stair Recognition

Computer vision can be used by robotic leg prostheses and exoskeletons to improve transitions between different locomotion modes (e.g., level-ground walking and stair ascent) via prediction of oncoming environmental states. We developed the StairNet dataset to support research and development in vision-based automated stair recognition. The dataset builds on the ExoNet database – the largest and most diverse open-source dataset of wearable camera images of real-world walking environments. StairNet contains ~515,000 labelled images from six of the twelve original ExoNet classes. These images were reclassified into four classes using new definitions with the goal of increasing the accuracy of the cutoff points between classes. The dataset was manually parsed several times during annotation to reduce misclassification errors and remove images with large obstructions. The StairNet dataset opens new opportunities for environment-adaptive control and path planning of robotic leg prostheses and exoskeletons. 

Reference:

1. Kurbis AG, Laschowski B, and Mihailidis A. (2022). Stair Recognition for Robotic Exoskeleton Control using Computer Vision and Deep Learning. IEEE International Conference on Rehabilitation Robotics (ICORR). Rotterdam, Netherlands. DOI: 10.1109/ICORR55369.2022.9896501.