Estimating Focused Pedestrian using Smooth-Pursuits Eye Movements and Point Cloud toward Assistive System for Wheelchair
Yuto Ito, Kentaro Takemura · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
Intelligent electric wheelchairs have been developed for personal mobility, and eye-gaze measurement is essential for comprehending the user's attention and for assisting operations. In previous studies, the gaze vector (i.e., the visual or optical axis of the eye) was projected onto the environmental map; hence, an eye tracker was installed on the wheelchair. In addition, hardware calibration, which determines the geometric relationship between the eye tracker and other sensors, such as LiDAR, was performed beforehand. Recently, wearable eye-trackers are expected to employ a daily-use device; therefore, the cooperation between sensors is essential without geometric constraints. Accordingly, we propose a method for estimating focused pedestrians using smooth-pursuit eye movements in the real world. Pedestrians are tracked using a point cloud obtained with 3D LiDAR, and the trajectories of the focused pedestrian are recorded on an environmental map constructed with simultaneous localization and mapping. Several experiments were conducted to evaluate the computational methods for the correlation between eye movements and moving objects, and we confirmed the feasibility and the current issues through these experiments.