Computer Science > Software Engineering
arXiv:2411.13346 (cs)
[Submitted on 20 Nov 2024]
Title:Gaze2AOI: Open Source Deep-learning Based System for Automatic Area of Interest Annotation with Eye Tracking Data
View a PDF of the paper titled Gaze2AOI: Open Source Deep-learning Based System for Automatic Area of Interest Annotation with Eye Tracking Data, by Karolina Trajkovska and 2 other authors
View PDFHTML (experimental)Abstract:Eye gaze is considered an important indicator for understanding and predicting user behaviour, as well as directing their attention across various domains including advertisement design, human-computer interaction and film viewing. In this paper, we present a novel method to enhance the analysis of user behaviour and attention by (i) augmenting video streams with automatically annotating and labelling areas of interest (AOIs), and (ii) integrating AOIs with collected eye gaze and fixation data. The tool provides key features such as time to first fixation, dwell time, and frequency of AOI revisits. By incorporating the YOLOv8 object tracking algorithm, the tool supports over 600 different object classes, providing a comprehensive set for a variety of video streams. This tool will be made available as open-source software, thereby contributing to broader research and development efforts in the field.
| Subjects: | Software Engineering (cs.SE) |
| Cite as: | arXiv:2411.13346 [cs.SE] |
| (orarXiv:2411.13346v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2411.13346 arXiv-issued DOI via DataCite |
Submission history
From: Karolina Trajkovska [view email][v1] Wed, 20 Nov 2024 14:17:23 UTC (11,251 KB)
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View a PDF of the paper titled Gaze2AOI: Open Source Deep-learning Based System for Automatic Area of Interest Annotation with Eye Tracking Data, by Karolina Trajkovska and 2 other authors
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