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Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

Gavindya Jayawardena, Sampath Jayarathna
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 16
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781799860488|DOI: 10.4018/IJMDEM.2021010104
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MLA

Jayawardena, Gavindya, and Sampath Jayarathna. "Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels." IJMDEM vol.12, no.1 2021: pp.49-64. http://doi.org/10.4018/IJMDEM.2021010104

APA

Jayawardena, G. & Jayarathna, S. (2021). Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels. International Journal of Multimedia Data Engineering and Management (IJMDEM), 12(1), 49-64. http://doi.org/10.4018/IJMDEM.2021010104

Chicago

Jayawardena, Gavindya, and Sampath Jayarathna. "Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels," International Journal of Multimedia Data Engineering and Management (IJMDEM) 12, no.1: 49-64. http://doi.org/10.4018/IJMDEM.2021010104

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Abstract

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements.

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