사용자 상호작용 기반의 시선 검출을 위한 비강압식 캘리브레이션
Non-intrusive Calibration for User Interaction based Gaze Estimation
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초록
본 논문에서는 웹 페이지 탐색 시 지속해서 발생하는 사용자 상호작용 과정을 이용하여 시선 검출을 위한 캘리브레이션 데이터를 획득하고, 사용자의 시선을 검출하는 동안 자연스럽게 캘리브레이션을 수행하는 방법에 관하여 기술하였다. 제안된 비강압식 캘리브레이션은 획득한 캘리브레이션 데이터를 이용하여 미리 학습된 시선 검출 CNN 모델을 새로운 사용자에 적응하도록 보정하는 과정이다. 이를 위해 훈련을 통해서 시선을 검출하는 일반화된 모델을 만들고 캘리브레이션에서는 온라인 학습 과정을 통해 빠르게 새로운 사용자에 적응하도록 하였다. 실험을 통하여 다양한 사용자 상호작용의 조합으로 시선 검출 모델을 캘리브레이션 하여 성능을 비교하였으며, 기존 방법 대비 개선된 정확도를 얻을 수 있었다.
Abstract
In this paper, we describe a new method for acquiring calibration data using a user interaction process, which occurs continuously during web browsing in gaze estimation, and for performing calibration naturally while estimating the user's gaze. The proposed non-intrusive calibration is a tuning process over the pre-trained gaze estimation model to adapt to a new user using the obtained data. To achieve this, a generalized CNN model for estimating gaze is trained, then the non-intrusive calibration is employed to adapt quickly to new users through online learning. In experiments, the gaze estimation model is calibrated with a combination of various user interactions to compare the performance, and improved accuracy is achieved compared to existing methods.
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