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  1. Proceedings of the 1st international workshop on pervasive eye tracking & mobile eye-based interaction (PETMEI '11)
  2. Semantic analysis of mobile eyetracking data
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Semantic analysis of mobile eyetracking data
Implementing gaze control for peripheral devices
The research framework of eye-tracking based mobile device usability evaluation
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Human factor affects eye movement pattern during riding motorcycle on the mountain
Towards a more effective method for analyzing mobile eye-tracking data: integrating gaze data with object recognition algorithms
Analysing EOG signal features for the discrimination of eye movements with wearable devices
Combining gaze with manual interaction to extend physical reach
Speed-accuracy trade-off in dwell-based eye pointing tasks at different cognitive levels
Saliency-based image editing for guiding visual attention

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Semantic analysis of mobile eyetracking data

Content Provider ACM Digital Library
Author Pelz, Jeff B.
Abstract Researchers using laboratory-based eyetracking systems now have access to sophisticated data-analysis tools to reduce raw gaze data, but the huge data sets coming from wearable eyetrackers cannot be analyzed with the same tools. The lack of constraints that make mobile systems such powerful tools prevent the analysis tools designed for static or tracked observers from working with freely moving observers. Proposed solutions have included infrared markers hidden in the scene to provide reference points, Simultaneous Localization and Mapping (SLAM), and multi-view geometry techniques that build models from multiple views of a scene. These methods map fixations onto predefined or extracted 3D scene models, allowing traditional static-scene analysis tools to be used. Another approach to analysis of mobile eyetracking data is to code fixations with semantically meaningful labels rather than mapping the fixations to fixed 3D locations. This offers two important advantages over the model-based methods; semantic mapping allows coding of dynamic scenes without the need to explicitly track objects, and it provides an inherently flexible and extensible object-based coding scheme.
Starting Page 1
Ending Page 2
Page Count 2
File Format PDF
ISBN 9781450309301
DOI 10.1145/2029956.2029958
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-09-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Data analysis Mobile eyetracking Eye movements
Content Type Text
Resource Type Article
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