In this paper, we address the intricate challenge of gaze vector prediction,
a pivotal task with applications ranging from human-computer interaction to
driver monitoring systems. Our innovative approach is designed for the
demanding setting of extremely low-light conditions, leveraging a novel
temporal event encoding scheme, and a dedicated neural network architecture.
The temporal encoding method seamlessly integrates Dynamic Vision Sensor (DVS)
events with grayscale guide frames, generating consecutively encoded images for
input into our neural network. This unique solution not only captures diverse
gaze responses from participants within the active age group but also
introduces a curated dataset tailored for low-light conditions. The encoded
temporal frames paired with our network showcase impressive spatial
localization and reliable gaze direction in their predictions. Achieving a
remarkable 100-pixel accuracy of 100%, our research underscores the potency of
our neural network to work with temporally consecutive encoded images for
precise gaze vector predictions in challenging low-light videos, contributing
to the advancement of gaze prediction technologies.

本文研究了注视向量预测的复杂挑战，重点是在极低光条件下的创新方法，通过使用一种新的时间事件编码方案和专用的神经网络架构，将动态视觉传感器（DVS）事件与灰度引导帧无缝集成，生成连续编码图像输入到神经网络中，这种独特的解决方案不仅捕捉了活跃年龄组参与者的多样化视线反应，还推出了专门针对低光条件的精选数据集。编码后的时间帧与我们的网络相配，展示了令人印象深刻的空间定位和可靠的注视方向预测。通过在具有挑战性的低光视频中精确预测时间连续编码图像的注视向量，取得了 100% 的 100 像素准确率，突显了我们神经网络在注视预测技术的进展中的潜力。