Real-time staircase detection from a wearable stereo system

We address the problem of staircase detection, in the context of a navigation aid for the visually impaired. The requirements for such a system are robustness to viewpoint, distance, scale, real-time operation, high detection rate and low false alarm rate. Our approach uses classifiers trained using Haar features and Ad-aboost learning. This first stage does detect staircases, but produces many false alarms. The false alarm rate is drastically reduced by using spatial context in the form of the estimated ground plane, and by enforcing temporal consistency. We have validated our approach on many real sequences under various weather conditions, and are presenting some of the quantitative results here.