Estimation of Person-Specific Visual Attention via Selection of Similar Persons
Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2020
This paper presents a method for estimation of person-specific visual attention based on estimated similar persons' visual attention. For improving the estimation performance of person-specific visual attention, the proposed method uses the dataset including the large number of images and corresponding gaze data of many persons not including the target person and trains an estimation model based on deep learning. By using the estimated visual attention of similar persons for the target image, the proposed method estimates the visual attention of the target person with the small amount of gaze data. Experimental results show that the proposed method is effective for estimation of person-specific visual attention.