Eye gaze estimation: A survey on deep learning-based approaches
Eye gaze estimation: A survey on deep learning-based approaches
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DOI：
10.1016/j.eswa.2022.116894
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Primesh Pathirana;Shashimal Senarath;D. Meedeniya;S. Jayarathna
中科院分区：
文献类型：
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作者：
Primesh Pathirana;Shashimal Senarath;D. Meedeniya;S. Jayarathna
人类视线估计通过识别用户的兴趣点，在人机交互和计算机视觉的许多应用中发挥着重要的作用。深度学习的革命性发展在凝视估计文献中引起了极大的关注。视线估计技术已经从单用户受限环境发展到多用户非受限环境，深度学习技术在复杂的非受限环境中具有广泛的适用性。本文对基于深度学习的单用户和多用户凝视估计方法进行了综述。分析了基于深度学习模型体系结构、坐标系、环境约束、数据集和性能评估指标的最新方法。这项调查的一个关键结果是认识到多用户凝视估计技术的局限性、挑战和未来发展方向。此外，本文还为未来的多用户视线估计研究提供了一个参考点和指导。
Human gaze estimation plays a major role in many applications in human–computer interaction and computer vision by identifying the users’ point-of-interest. Revolutionary developments of deep learning have captured significant attention in gaze estimation literature. Gaze estimation techniques have progressed from single-user constrained environments to multi-user unconstrained environments with the applicability of deep learning techniques in complex unconstrained environments with extensive variations. This paper presents a comprehensive survey of the single-user and multi-user gaze estimation approaches with deep learning. State-of-the-art approaches are analyzed based on deep learning model architectures, coordinate systems, environmental constraints, datasets and performance evaluation metrics. A key outcome from this survey realizes the limitations, challenges and future directions of multi-user gaze estimation techniques. Furthermore, this paper serves as a reference point and a guideline for future multi-user gaze estimation research.