Crosswalk Detection from Inertial Data for Visually Impaired People

The popularity of lightweight portable sensors has made inertial data from everyday activities widely available, sparkling interest in tracking these activities. This study focuses on detecting the motion patterns of visually impaired people while crossing a crosswalk, which is observed to be different from sighted people. Visually impaired individuals exhibit different behaviors, such as using a cane to find a crosswalk terminal, resulting in unique patterns in inertial data. This work proposes a crosswalk detection method based on inertial signals captured from naturally walking visually impaired pedestrians. Crosswalk crossings are long-lasting actions and rare events in a walking trajectory, introducing questions on which features to use and which parts of the data to investigate. We handle this problem by efficiently tuning Deep Convolutional Neural Networks with their multi-resolution feature extraction capability. Considering also the detected crosswalks in the correction of dead-reckoning based positioning applications, we state that the false detections are intolerable. Thus, we also perform precision-driven training processes, in which the weighted F-score and related surrogate loss functions are employed. The results demonstrate that the proposed approaches detect crosswalks effectively and show potential in classifying other similar long-duration actions.