Learning-Based Detection of Events in Eye-Tracking Data: An Investigation Into Small-Scale Models for Automotive Applications
| dc.contributor.author | Due, Martin | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Ringh, Axel | |
| dc.contributor.supervisor | Molin, Vincent | |
| dc.contributor.supervisor | Zemblys, Raimondas | |
| dc.date.accessioned | 2024-11-20T13:25:24Z | |
| dc.date.available | 2024-11-20T13:25:24Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | Detection of eye movement events in eye-tracking data is integral to various research fields and commercial applications. Traditionally, the detection has been accomplished either by hand or with threshold based detectors with the inherent drawback that thresholds levels and other parameters had to be hand picked for each scenario. In recent years machine learning methods have been employed for eye movement event detection that do away with that requirement. However, these models tend to be too large to run on limited resources in embedded applications, particularly automotive ones. This thesis focuses on creating models that are smaller but with retained performance. Five machine learning methods were evaluated and hyperparameters were tuned to create well performing small models. Furthermore, the usage of synthetic data for training was investigated, both as a supplement to real data and as a sole source of training data. The study found that a Multilayer Perceptron model (MLP) trained on a combination of real and synthetic data struck the best balance between size and performance. Additionally, results show that models trained purely on synthetic data also performed reasonably well. The findings of this thesis suggest that small, efficient models can effectively detect eye movement events, with potential applications in automotive contexts. | |
| dc.identifier.coursecode | MVEX03 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/308999 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Eye movements, Event detection, Fixations, Saccades, Machine Learning, Model Optimization. | |
| dc.title | Learning-Based Detection of Events in Eye-Tracking Data: An Investigation Into Small-Scale Models for Automotive Applications | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Engineering mathematics and computational science (MPENM), MSc |