TY - JOUR
T1 - Automated object detection in mobile eye-tracking research
T2 - comparing manual coding with tag detection, shape detection, matching, and machine learning
AU - Segijn, Claire M.
AU - Menheer, Pernu
AU - Lee, Garim
AU - Kim, Eunah
AU - Olsen, David
AU - Hofelich Mohr, Alicia
N1 - Publisher Copyright:
© 2024 Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - The goal of the current study is to compare different methods for automated object detection (i.e. tag detection, shape detection, matching, and machine learning) with manual coding on different types of objects (i.e. static, dynamic, and dynamic with human interaction) and describe the advantages and limitations of each method. We tested the methods in an experiment that utilizes mobile eye tracking because of the importance of attention in communication science and the challenges posed by this type of data when analyzing different objects because visual parameters are consistently changing within and between participants. Machine learning was found to be the most reliable method to detect all types of objects and was slightly more conservative compared to manual coding. Feature-based matching worked well for static objects. We discuss the advantages and challenges of each method along with key considerations for researchers depending on their research objective, the type of object, and the object detection method they will use.
AB - The goal of the current study is to compare different methods for automated object detection (i.e. tag detection, shape detection, matching, and machine learning) with manual coding on different types of objects (i.e. static, dynamic, and dynamic with human interaction) and describe the advantages and limitations of each method. We tested the methods in an experiment that utilizes mobile eye tracking because of the importance of attention in communication science and the challenges posed by this type of data when analyzing different objects because visual parameters are consistently changing within and between participants. Machine learning was found to be the most reliable method to detect all types of objects and was slightly more conservative compared to manual coding. Feature-based matching worked well for static objects. We discuss the advantages and challenges of each method along with key considerations for researchers depending on their research objective, the type of object, and the object detection method they will use.
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U2 - 10.1080/19312458.2024.2443396
DO - 10.1080/19312458.2024.2443396
M3 - Article
AN - SCOPUS:85212984252
SN - 1931-2458
JO - Communication Methods and Measures
JF - Communication Methods and Measures
ER -