Intersection Perception Through Real-Time Semantic Segmentation to Assist Navigation of Visually Impaired Pedestrians

Intersection navigation comprises one of the major ingredient of Intelligent Transportation Systems (ITS) for Visually Impaired Pedestrians (VIP), who are the most vulnerable road users that should be protected with a high priority in metropolitan areas. Robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors or depth sensors. These dividual solutions have reached impressive detectable range and accuracy with relatively short running time, and enhanced the intersection perception to a large degree. However, simultaneously enabling all detectors incurs a long delay and becomes computationally prohibitive on wearable embedded systems. In this work, we propose to seize CNN-based per-pixel semantic segmenter to cover navigational perception needs in a unified way. This is not only critical to perceive crosswalk position (where to cross roads), traffic light signal (when to cross roads), but also to analyze the states of other pedestrians and vehicles (whether safe to cross roads). At the centroid of our unification proposal is a deep learning architecture, aspired to attain efficient and robust semantic understanding. A comprehensive variety of experiments demonstrates the advanced accuracy over state-of-art algorithms/segmenters while maintaining high inference speed on a real-world navigation assistance system.