Exemplar-Based Pseudo-Viewpoint Rotation for White-Cane User Recognition from a 2D Human Pose Sequence

Abstract

In recent years, various facilities are equipped to support visually impaired people, but accidents caused by visual disabilities still occur. In this paper, to support the visually-impaired people in a public space, we aim to classify whether a pedestrian image sequence obtained by a surveillance camera is a white-cane user or not from the temporal transition of a human pose represented as 2D coordinates. However, since the appearance of the 2D pose varies largely depending on the viewpoint of the pose, it is difficult to classify them. So, in this paper, we propose a method to rotate the viewpoint of a pose from various pseudo-viewpoints based on a pair of 2D poses simultaneously observed and classify the sequence by multiple classifiers corresponding to each viewpoint. Viewpoint rotation makes it possible to obtain pseudo-poses seen from various pseudo-viewpoints, extract richer pose features, and recognize white-cane users more accurately. Through an experiment, we confirmed that the proposed method improves the recognition rate by 12% compared to the method not employing viewpoint rotation.