title: Real-time Pedestrian Crossing Recognition for Assistive Outdoor Navigation.
abstract: Navigation in urban environments can be difficult for people who are blind or visually impaired. In this project, we present a system and algorithms for recognizing pedestrian crossings in outdoor environments. Our goal is to provide navigation cues for crossing the street and reaching an island or sidewalk safely. Using a state-of-the-art Multisense S7S sensor, we collected 3D pointcloud data for real-time detection of pedestrian crossing and generation of directional guidance. We demonstrate improvements to a baseline, monocular-camera-based system by integrating 3D spatial prior information extracted from the pointcloud. Our system's parameters can be set to the actual dimensions of real-world settings, which enables robustness of occlusion and perspective transformation. The system works especially well in non-occlusion situations, and is reasonably accurate under different kind of conditions. As well, our large dataset of pedestrian crossings, organized by different types and situations of pedestrian crossings in order to reflect real-word environments, is publicly available in a commonly used format (ROS bagfiles) for further research.
authors: University of Pisa.
authors: Simone
authors: S
authors: Fontanesi
authors: University of Pisa.
authors: Alessandro
authors: A
authors: Frigerio
authors: University of Pisa.
authors: Luca
authors: L
authors: Fanucci
authors: Massachusetts Institute of Technology.
authors: William
authors: W
authors: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001766:Blindness / Q000534:rehabilitation*; D000067917:Cloud Computing; D003199:Computer Systems; D003463:Cues; D066264:Datasets as Topic; D006801:Humans; D021621:Imaging, Three-Dimensional; D000069636:Pedestrians; D012371:Robotics; D012985:Software Design; D014505:Urban Population; D014786:Vision Disorders / Q000534:rehabilitation*; D000076251:Wearable Electronic Devices