@article{,
title= {Identification of Radar Interpretation on Basis of Eye Tracking Data},
journal= {},
author= {Stratmann, Tim Claudius and Kock, Berislaw and Boll, Susanne},
year= {2019},
url= {},
abstract= {Radar observation is an essential component of maritime navigation. There is a possibility with a wide range of tasks and activities, that navigator's eye movements are on the radar, but he does not pay enough attention for sufficient interpretation. This increases collision risk. We introduce a algorithmic model, which may differ between an interpretation and no interpretation of a radar video on the basis of eye tracking data. Corresponding data is collected in a study, its design is inspired by real navigation. We extract several features and use machine learning to identify dependencies and impacts to classify eye tracking data. Results of various algorithms and calculations were compared to derive the final model. We conclude that logistic regression with a L-BFGS solver has highest accuracy and recall, can be calculated quickly and is easy to interpret. This torrent contains the acquired anonymous training and test data, as well as a description of our trained model in R.},
keywords= {l-bfgs solver, eye tracking, gaze, radar interpretation, maritime},
terms= {},
license= {},
superseded= {}
}