Traditional eye tracking requires specialized hardware, which means
collecting gaze data from many observers is expensive, tedious and slow.
Therefore, existing saliency prediction datasets are order-of-magnitudes
smaller than typical datasets for other vision recognition tasks. The small
size of these datasets limits the potential for training data intensive
algorithms, and causes overfitting in benchmark evaluation. To address this
deficiency, this paper introduces a webcam-based gaze tracking system that
supports large-scale, crowdsourced eye tracking deployed on Amazon Mechanical
Turk (AMTurk). By a combination of careful algorithm and gaming protocol
design, our system obtains eye tracking data for saliency prediction comparable
to data gathered in a traditional lab setting, with relatively lower cost and
less effort on the part of the researchers. Using this tool, we build a
saliency dataset for a large number of natural images. We will open-source our
tool and provide a web server where researchers can upload their images to get
eye tracking results from AMTurk.

本文介绍了一种基于网络摄像头的注视追踪系统，支持大规模的众包眼动追踪。该系统通过谨慎的算法和游戏协议设计，以相对较低的成本和研究人员的少量努力获得了与传统实验室设置相当的视觉显著性预测数据。使用该工具，作者建立了一个自然图像的显著性数据集，并提供了一个 Web 服务器，研究人员可以上传图像以从 AMTurk 获取眼动追踪结果。