Crowdsourcing Gaze Data Collection

Knowing where people look is a useful tool in many
various image and video applications. However, traditional gaze tracking hardware is expensive and requires
local study participants, so acquiring gaze location data
from a large number of participants is very problematic. In this work we propose a crowdsourced method
for acquisition of gaze direction data from a virtually
unlimited number of participants, using a robust selfreporting mechanism (see Figure 1). Our system collects
temporally sparse but spatially dense points-of-attention
in any visual information. We apply our approach to an
existing video data set and demonstrate that we obtain
results similar to traditional gaze tracking. We also explore the parameter ranges of our method, and collect
gaze tracking data for a large set of YouTube videos.