In the pursuit of (ground) truth: a hand-labelling tool for eye movements recorded during dynamic scene viewing

We here present parts of our ongoing work to facilitate the largescale analysis of smooth pursuit eye movements made while viewing dynamic natural scenes. Classification of smooth pursuit episodes can be difficult in the presence of eye-tracking noise, and we thus recently proposed an algorithm that clusters gaze recordings from several observers in order to improve classification robustness. We now implemented a publicly available tool that allows for generation of a ground truth benchmark by assisted handlabelling of video gaze data. Based on the labelling produced with the tool we present preliminary evaluation results for our smooth pursuit classification approach in comparison to state-of-the-art algorithms. Overall, human observers spend more than 12% of their viewing time performing smooth pursuit, which emphasizes the importance of investigating smooth pursuit behaviour in naturalistic contexts.