高效实现的视线估计在低功耗/低质量消费者成像系统上 Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems

准确有效的眼睛注视估计对于新兴的消费电子系统如驾驶员监控系统和新颖的用户界面非常重要。这些系统需要在低功耗和低成本的困难,不受限制的环境中可靠运行。在本文中,引入了一种新的硬件友好的,具有最小计算要求的卷积神经网络模型,并对基于外观的有效注视估计进行评估。该模型经过测试并与现有的基于CNN方法的外观进行比较,以更少的计算要求实现更好的视线注视准确性。

Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper a new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance based CNN approaches, achieving better eye gaze accuracy with significantly fewer computational requirements.

https://arxiv.org/pdf/1806.10890v1

转载请注明:《高效实现的视线估计在低功耗/低质量消费者成像系统上 Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems

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