The performance of driver gaze detection by video-based eye-tracking often encounters problems in lowcomputing speed, high-power consumption, and installation space constraints inside the vehicle. In this paper, we present an eye-tracking system that uses a single field-programmable-gate-array chip to overcome the aforementioned problems. In the detection system, the image quality is 640 \(\times\) 480 pixels with an 80 fps frame rate. Eye feature extraction is conducted using the enhanced semantics-based vague image representation approach. A succinct fully-connected neural network is then employed to classify various directions of sightline. Our experimental results exhibited a noticeable recognition speed at 0.52 \(\upmu\)s using a 100 MHz system clock and had an average detection rate of 92%.

通过基于视频的眼动跟踪进行驾驶员凝视检测的性能通常会遇到计算速度低，功耗高以及车辆内部安装空间受限的问题。在本文中，我们提出了一种眼动追踪系统，该系统使用单个现场可编程门阵列芯片来克服上述问题。在检测系统中，图像质量为640 \（\ times \） 480像素，帧率为80 fps。使用增强的基于语义的模糊图像表示方法进行眼睛特征提取。然后，使用简洁的全连接神经网络对视线的各个方向进行分类。我们的实验结果显示出明显的识别速度，为0.52 \（\ upmu \）使用100 MHz系统时钟，平均检测率为92％。