Measuring Task Performance Using Gaze Regions

Irwandi, Hipiny and Hamimah, Ujir (2015) Measuring Task Performance Using Gaze Regions. In: 2015 9th International Conference on IT in Asia (CITA) : Transforming Big Data into Knowledge, 4-5 August 2015, Kuching, Sarawak Malaysia.

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Abstract

We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification’s accuracy on several proposed schemes.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: component; Histogram of Oriented Gradients feature descriptor, Gaze Regions, Egocentric, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education,research, Universiti Malaysia Sarawak
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 08 Sep 2016 19:44
Last Modified: 14 Feb 2017 07:28
URI: http://ir.unimas.my/id/eprint/13449

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