High-speed gaze detection using a single FPGA for driver assistance systems

摘要

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 640x480 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 mu s using a 100 MHz system clock and had an average detection rate of 92%.