Crowdsourcing Gaze Data Collection

TL;DR

This paper introduces a crowdsourced method for collecting gaze data using a self-reporting mechanism, enabling large-scale, cost-effective gaze tracking comparable to traditional hardware-based methods.

Contribution

It presents a novel crowdsourcing approach for gaze data collection that is scalable and does not require specialized hardware.

Findings

Results comparable to traditional gaze tracking methods

Collected gaze data for a large set of YouTube videos

Explored parameter ranges for the proposed method

Abstract

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.