ArticleSensors (Basel, Switzerland)2021
Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification.Journal of eye movement research · 2025Article
- I-MPN: inductive message passing network for efficient human-in-the-loop annotation of mobile eye tracking data.Scientific reports · 2025Article
- The fundamentals of eye tracking part 3: How to choose an eye tracker.Behavior research methods · 2025Article
- Towards Automatic Object Detection and Activity Recognition in Indoor Climbing.Sensors (Basel, Switzerland) · 2024Article
- MYFix: Automated Fixation Annotation of Eye-Tracking Videos.Sensors (Basel, Switzerland) · 2024Article
- Quantifying Dwell Time With Location-based Augmented Reality: Dynamic AOI Analysis on Mobile Eye Tracking Data With Vision Transformer.Journal of eye movement research · 2024Article
- Cow detection and tracking system utilizing multi-feature tracking algorithm.Scientific reports · 2023Article
- Shifting Perspectives: A proposed framework for analyzing head-mounted eye-tracking data with dynamic areas of interest and dynamic scenes.Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting · 2023Article
- Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing.Foods (Basel, Switzerland) · 2022Article
- Platelet Detection Based on Improved YOLO_v3.Cyborg and bionic systems (Washington, D.C.) · 2022Article
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Authors and funding
6 authors.
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Abstract
Remote eye tracking has become an important tool for the online analysis of learning processes. Mobile eye trackers can even extend the range of opportunities (in comparison to stationary eye trackers) to real settings, such as classrooms or experimental lab courses. However, the complex and sometimes manual analysis of mobile eye-tracking data often hinders the realization of extensive studies, as this is a very time-consuming process and usually not feasible for real-world situations in which participants move or manipulate objects. In this work, we explore the opportunities to use object recognition models to assign mobile eye-tracking data for real objects during an authentic students' lab course. In a comparison of three different Convolutional Neural Networks (CNN), a Faster Region-Based-CNN, you only look once (YOLO) v3, and YOLO v4, we found that YOLO v4, together with an optical flow estimation, provides the fastest results with the highest accuracy for object detection in this setting. The automatic assignment of the gaze data to real objects simplifies the time-consuming analysis of mobile eye-tracking data and offers an opportunity for real-time system responses to the user's gaze. Additionally, we identify and discuss several problems in using object detection for mobile eye-tracking data that need to be considered.
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