https://scholars.lib.ntu.edu.tw/handle/123456789/637404
標題: | 360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales | 作者: | Ashesh CHU-SONG CHEN HSUAN-TIEN LIN |
公開日期: | 1-一月-2021 | 來源出版物: | 32nd British Machine Vision Conference, BMVC 2021 | 摘要: | Gaze estimation involves predicting where the person is looking at within an image or video. Technically, the gaze information can be inferred from two different magnification levels: face orientation and eye orientation. The inference is not always feasible for gaze estimation in the wild, given the lack of clear eye patches in conditions like extreme left/right gazes or occlusions. In this work, we design a model that mimics humans' ability to estimate the gaze by aggregating from focused looks, each at a different magnification level of the face area. The model avoids the need to extract clear eye patches and at the same time addresses another important issue of face-scale variation for gaze estimation in the wild. We further extend the model to handle the challenging task of 360-degree gaze estimation by encoding the backward gazes in the polar representation along with a robust averaging scheme. Experiment results on the ETH-XGaze dataset, which does not contain scale-varying faces, demonstrate the model's effectiveness to assimilate information from multiple scales. For other benchmark datasets with many scale-varying faces (Gaze360 and RT-GENE), the proposed model achieves state-of-the-art performance for gaze estimation when using either images or videos. Our code and pretrained models can be accessed at https://github.com/ashesh-0/MultiZoomGaze. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637404 |
顯示於: | 資訊工程學系 |
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