关键词: 眼动跟踪数据, 数据融合, 卷积神经网络（CNN）, 双向长短时记忆网络（BLSTM）

Abstract:

For traditional data fusion algorithms, the fusion effect of eye movement and tracking data in multiple scenarios is poor. This paper proposes a new eye movement and tracking data fusion algorithm based on deep learning, namely Eye-CNN BLSTM algorithm. Firstly, the algorithm adds new artificial features based on the spatial position information of the original eye movement and tracking data. Secondly, CNN（Convolutional Neural Network） and BLSTM（Bi-directional Long Short-Term Memory） are combined to design a new fusion structure. Finally, the experimental results show that compared with six classic data fusion algorithms, the fusion performance of the proposed algorithm is better on OTB-100 dataset.

Key words: eye movement and tracking data, data fusion, Convolutional Neural Network（CNN）, Bi-directional Long Short-Term Memory（BLSTM）