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DOI: 10.3791/58459-v
Marco Amati1,2, Chris McCarthy3, Ebadat Ghanbari Parmehr4, Jodi Sita5
1Visiting Professor, Dipartimento di Scienze Agro-Ambientali e Territoriali,Università degli Studi di Bari, 2Centre for Urban Research,Royal Melbourne Institute of Technology (RMIT University), 3School of Software and Electrical Engineering,Swinburne University of Technology, 4Faculty of Civil Engineering,Babol Noshirvani University of Technology, 5School of Science,Australian Catholic University
The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.
Many eye tracking studies rely on complex video stimuli and real world settings making analysis of the data highly complex. This analysis technique allows for a much richer and automated approach to analyzing video-based data than currently available methods leading to richer extraction of more complex data. This method could be used in many different eye tracking applications particularly in real world situations or those that use video as a stimulus.
Landscape studies have relied on understanding how people react to different visual stimuli. This technique combined with eye tracking could be used to test these assumptions. For this type of research, a team approach is essential for there are multiple aspects that require high level input and consideration.
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