Combining eye-tracking data with an analysis of video content from free-viewing a video of a walk in an urban park environment

Journal article


Amati, Marco, McCarthy, Chris, Parmehr, Ebadat Ghanbari and Sita, Jodi. (2019). Combining eye-tracking data with an analysis of video content from free-viewing a video of a walk in an urban park environment. Journal of Visualized Experiments. (147), p. e58459.
AuthorsAmati, Marco, McCarthy, Chris, Parmehr, Ebadat Ghanbari and Sita, Jodi
Abstract

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.

The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants' eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

Year2019
JournalJournal of Visualized Experiments
Journal citation(147), p. e58459
PublisherJournal of Visualized Experiments
Page range1-9
FunderAustralian Research Council (ARC)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication process dates
Deposited13 May 2021
Grant IDARC/DP150103135
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