头眼数据融合注视方向研究

Xin Xu, Changyuan Wang
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引用次数: 0

摘要

摘要视线是指人眼的注视方向,反映了人的注意力的焦点。头部运动是人类注视过程中重要的伴随行为,对人类的视觉注意具有重要意义。本文拟将头部运动和眼动的注视焦点计算建模与深度学习数据融合相结合。将数据融合的凝视方向计算模型与神经网络算法相结合,利用深度学习技术揭示头部运动与眼动之间的关系,将头部运动与眼动数据进行融合,实现准确、快速的实时凝视空间方向计算。提高注视跟踪系统的效率、可靠性、可用性和功能性的新思路。本文采用卷积神经网络方法,在头部姿态自由的情况下,视线方向的分类准确率达到99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Gaze Direction of Head-Eye Data Fusion
Abstract The line of sight refers to the gaze direction of the human eye and reflects the focus of human attention. Head movement is an important accompanying behavior in the process of human gaze, and it is of great significance to human visual attention. This paper intends to combine the gaze focus calculation modeling of head movement and eye movement combined with deep learning data fusion. By combining the gaze direction calculation model of data fusion and neural network algorithm, deep learning technology is used to reveal the relationship between head movement and eye movement, and the data of head movement and eye movement are merged to realize accurate and fast real-time gaze spatial direction calculation. New ideas for improving the efficiency, reliability, usability and functionality of the gaze tracking system. In this paper, the convolutional neural network method is used, and the classification accuracy of the line of sight direction reaches 99% when the head posture is free.
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