运用信息建模技术理解眼动注视估计方法的研究趋势：一项自动化综述
Use of information modelling techniques to understand research trends in eye gaze estimation methods: An automated review.
作者信息
Singh Jaiteg, Modi Nandini
机构信息
Department of Computer Applications, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India.
Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, 140401, India.
出版信息
Heliyon. 2019 Dec 18;5(12):e03033. doi: 10.1016/j.heliyon.2019.e03033. eCollection 2019 Dec.
Eye gaze tracking has been used to study the influence of visual stimuli on consumer behavior and attentional processes. Eye gaze tracking techniques have made substantial contributions in advertisement design, human computer interaction, virtual reality and disease diagnosis. Eye gaze estimation is considered critical for prediction of human attention, and hence indispensable for better understanding human activities. In this paper, Latent Semantic Analysis is used to develop an information model for identifying emerging research trends within eye gaze estimation techniques. An exhaustive collection of 423 titles and abstracts of research papers published during 2005-2018 were used. Five major research areas and ten research trends were classified based upon this study.
眼动追踪已被用于研究视觉刺激对消费者行为和注意力过程的影响。眼动追踪技术在广告设计、人机交互、虚拟现实和疾病诊断等方面做出了重大贡献。眼动估计被认为对预测人类注意力至关重要，因此对于更好地理解人类活动不可或缺。本文利用潜在语义分析开发了一个信息模型，用于识别眼动估计技术中新兴的研究趋势。研究使用了2005年至2018年期间发表的423篇研究论文的标题和摘要进行了详尽收集。基于这项研究，划分出了五个主要研究领域和十个研究趋势。
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