Crowdsourcing Gaze Data Collection
This addresses the issue of acquiring gaze data at scale for image and video applications, though it appears incremental as it builds on existing methods with a new collection approach.
The authors tackled the problem of expensive and limited gaze data collection by proposing a crowdsourced method using self-reporting, which achieved results similar to traditional gaze tracking on an existing video dataset.
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 self-reporting 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.