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Will You Take This Turn? Gaze-Based Turning Activity Recognition During Navigation

Authors Negar Alinaghi , Markus Kattenbeck , Antonia Golab, Ioannis Giannopoulos

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Negar Alinaghi
  • Geoinformation, TU Wien, Austria
Markus Kattenbeck
  • Geoinformation, TU Wien, Austria
Antonia Golab
  • Geoinformation, TU Wien, Austria
Ioannis Giannopoulos
  • Geoinformation, TU Wien, Austria
  • Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria

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Negar Alinaghi, Markus Kattenbeck, Antonia Golab, and Ioannis Giannopoulos. Will You Take This Turn? Gaze-Based Turning Activity Recognition During Navigation. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.GIScience.2021.II.5

Abstract

Decision making is an integral part of wayfinding and people progressively use navigation systems to facilitate this task. The primary decision, which is also the main source of navigation error, is about the turning activity, i.e., to decide either to turn left or right or continue straight forward. The fundamental step to deal with this error, before applying any preventive approaches, e.g., providing more information, or any compensatory solutions, e.g., pre-calculating alternative routes, could be to predict and recognize the potential turning activity. This paper aims to address this step by predicting the turning decision of pedestrian wayfinders, before the actual action takes place, using primarily gaze-based features. Applying Machine Learning methods, the results of the presented experiment demonstrate an overall accuracy of 91% within three seconds before arriving at a decision point. Beyond the application perspective, our findings also shed light on the cognitive processes of decision making as reflected by the wayfinder’s gaze behaviour: incorporating environmental and user-related factors to the model, results in a noticeable change with respect to the importance of visual search features in turn activity recognition.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Activity recognition and understanding
  • Computing methodologies → Supervised learning by classification
Keywords
  • Activity Recognition
  • Wayfinding
  • Eye Tracking
  • Machine Learning

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