Paper Info
Title
ISeeCube: visual analysis of gaze data for video
Abstract
We introduce a new design for the visual analysis of eye tracking data recorded from dynamic stimuli such as video. ISeeCube includes multiple coordinated views to support different aspects of various analysis tasks. It combines methods for the spatiotemporal analysis of gaze data recorded from unlabeled videos as well as the possibility to annotate and investigate dynamic Areas of Interest (AOIs). A static overview of the complete data set is provided by a space-time cube visualization that shows gaze points with density-based color mapping and spatiotemporal clustering of the data. A timeline visualization supports the analysis of dynamic AOIs and the viewers' attention on them. AOI-based scanpaths of different viewers can be clustered by their Levenshtein distance, an attention map, or the transitions between AOIs. With the provided visual analytics techniques, the exploration of eye tracking data recorded from several viewers is supported for a wide range of analysis tasks.
Year
DOI
Venue
2014
10.1145/2578153.2616602
ETRA
Keywords
Field
DocType
different aspect,dynamic Areas,complete data set,various analysis task,analysis task,visual analysis,spatiotemporal analysis,dynamic AOIs,attention map,dynamic stimulus
Computer vision,Color mapping,Gaze,Visualization,Computer science,Levenshtein distance,Visual analytics,Timeline,Eye tracking,Artificial intelligence,Cluster analysis
Conference
Citations 
PageRank 
References 
23
0.97
18
Authors
3
Name
Order
Citations
PageRank
Kuno Kurzhals122720.63
Florian Heimerl225215.26
Daniel Weiskopf32988204.30