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Evaluation of Scanpath Comparison Metrics for Static and Dynamic Tasks

  • Purpose Automated scanpath comparison metrics should deliver an objective method to evaluate the similarity of scanpaths. The aim of this thesis is an evaluation of seven existing scanpath comparison metrics in static and dynamic tasks in order to provide a guidline that helps to decide which algorithm has to be chosen for a special kind of task. Methods The applicability of the algorithms for a static, visual search task and a dynamic, interactive video game task as well as their constraints and limitations were tested. Therefore, binocular gaze data were recorded by using the eye tracking system The Eye Tribe (The Eye Tribe ApS, Copenhagen/ Denmark). Objective task performance measures from 21 subjects were used in order to create scanpath groupings for which a relevant effect of dissimilarity was to be expected. Objective task performance measures such as task performance time were statistically evaluated and compared to the results gained by the comparison metrics. Results Four of the algorithms being used successfully identified differences for static and dynamic tasks: MultiMatch, iComp, SubsMatch and the Hidden Markov Model. ScanMatch was very sensitive for the static task but not applicable to the dynamic task whereas FuncSim was suitable for dynamic but not for static tasks. Eyenalysis failed to detect any effect. Conclusion The applicability of scanpath comparison metrics depends on the state of the task, respectively on the kind of experimental set up. In future, the application area for eye tracking will expand and an improvement of automated scanpath comparison metrics is therefore required.

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Author:Colleen Rothe
Advisor:Ulrich Schiefer, Enkelejda Kasneci
Document Type:Master's Thesis
Date of Publication (online):2016/01/11
Publishing Institution:Hochschule Aalen
Granting Institution:Hochschule Aalen, Optik und Mechatronik
Date of final exam:2015/05/06
Release Date:2016/01/11
Tag:Comparison metrics; Eye tracking; Fixation; Scanpath; Visual search
GND Keyword:Mustererkennung
Faculty:Optik und Mechatronik
Open Access:Open Access
Licence (German):License LogoVer√∂ffentlichungsvertrag f√ľr Publikationen mit Print on Demand