Robust fixation detection is essential for analysis of mobile eye tracking data in research and for gaze-based interaction. However, most fixation detection algorithms are optimized for remote eye tracking which makes them less robust in mobile settings with users moving around. A recent approach for mobile settings addresses this issue by incorporating visual similarity of gaze targets to identify fixation events more robustly [1]. But, it was not evaluated in real-time interactive settings. Your task in this thesis will be to implement the proposed algorithm (and optionally an improved version) and to systematically evaluate it in mobile and natural interaction settings.

[1] Julian Steil, Michael Xuelin Huang, and Andreas Bulling. 2018. Fixation detection for head-mounted eye tracking based on visual similarity of gaze targets. In Eye Tracking Research and Applications Symposium (ETRA), 1–9. https://doi.org/10.1145/3204493.3204538 

Contact: michael.barz@dfki.de