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Article

Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze

by 1,2,* and 1,2
1
German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Stuhlsatzenhausweg 3, Saarland Informatics Campus D3_2, 66123 Saarbrücken, Germany
2
Applied Artificial Intelligence, Oldenburg University, Marie-Curie Str. 1, 26129 Oldenburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Jamie A. Ward
Sensors 2021, 21(12), 4143; https://doi.org/10.3390/s21124143
Received: 12 May 2021 / Revised: 11 June 2021 / Accepted: 12 June 2021 / Published: 16 June 2021
(This article belongs to the Special Issue Wearable Technologies and Applications for Eye Tracking)
Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious manual annotation task, the automatic detection and annotation of visual attention to AOIs can accelerate and objectify eye tracking research, in particular for mobile eye tracking with egocentric video feeds. In this work, we implement two methods to automatically detect visual attention to AOIs using pre-trained deep learning models for image classification and object detection. Furthermore, we develop an evaluation framework based on the VISUS dataset and well-known performance metrics from the field of activity recognition. We systematically evaluate our methods within this framework, discuss potentials and limitations, and propose ways to improve the performance of future automatic visual attention detection methods. View Full-Text
Keywords: eye tracking; visual attention; eye tracking data analysis; area of interest; computer vision eye tracking; visual attention; eye tracking data analysis; area of interest; computer vision
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MDPI and ACS Style

Barz, M.; Sonntag, D. Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze. Sensors 2021, 21, 4143. https://doi.org/10.3390/s21124143

AMA Style

Barz M, Sonntag D. Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze. Sensors. 2021; 21(12):4143. https://doi.org/10.3390/s21124143

Chicago/Turabian Style

Barz, Michael, and Daniel Sonntag. 2021. "Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze" Sensors 21, no. 12: 4143. https://doi.org/10.3390/s21124143

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