VR Sickness assessment with perception prior and hybrid temporal features
Journal
Proceedings - International Conference on Pattern Recognition
Pages
5558-5564
Date Issued
2020
Author(s)
Abstract
Virtual reality (VR) sickness is one of the obstacles hindering the growth of the VR market. Different VR contents may cause various degree of sickness. If the degree of the sickness can be estimated objectively, it adds a great value and help in designing the VR contents. To address this problem, a novel content-based VR sickness assessment method which considers both the perception prior and hybrid temporal features is proposed. Based on the perception prior which assumes the user's field of view becomes narrower while watching videos, a Gaussian weighted optical flow is calculated with a specified aspect ratio. In order to capture the dynamic characteristics, hybrid temporal features including horizontal motion, vertical motion and the proposed motion anisotropy are adopted. In addition, a new dataset is compiled with one hundred VR sickness test samples and each of which comes along with the Discomfort Scores (DS) answered by the user and a Simulator Sickness Questionnaire (SSQ) collected at the end of test. A random forest regressor is then trained on this dataset by feeding the hybrid temporal features of both the present and the previous minute. Extensive experiments are conducted on the VRSA dataset and the results demonstrate that the proposed method is comparable to the state-of-the-art method in terms of effectiveness and efficiency. © 2021 IEEE
Subjects
Perception prior; Random forest; VR sickness assessment
Other Subjects
Aspect ratio; Decision trees; Diseases; Optical flows; Pattern recognition; Statistical tests; Dynamic characteristics; Effectiveness and efficiencies; Field of views; Horizontal motion; Simulator sickness; State-of-the-art methods; Temporal features; Vertical motions; Virtual reality
Type
conference paper