Evaluation of Attention-Guiding Video Visualization

We investigate four different variants of attention‐guiding video visualization techniques that aim to help users distribute their attention equally among potential objects of interest: bounding box visualization, force‐directed visualization, top‐down visualization, grid visualization. Objects of i...

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Vydáno v:Computer graphics forum Ročník 32; číslo 3pt1; s. 51 - 60
Hlavní autoři: Kurzhals, K., Höferlin, M., Weiskopf, D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford, UK Blackwell Publishing Ltd 01.06.2013
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ISSN:0167-7055, 1467-8659
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Abstract We investigate four different variants of attention‐guiding video visualization techniques that aim to help users distribute their attention equally among potential objects of interest: bounding box visualization, force‐directed visualization, top‐down visualization, grid visualization. Objects of interest are highlighted by rectangular shapes and then we concentrate on the manipulation of color, motion, and size. We conducted a controlled laboratory user study (n=25) to compare the four visualization techniques and the unmodified video material as baseline. We evaluated task performance and distribution of attention in a search task. These two properties become especially important when video material with numerous objects has to be observed. The distribution of attention was measured by eye tracking. Our results show that a more even distribution of attention between the objects can be achieved by attention‐guiding visualization, compared to unmodified video. Many participants feel more comfortable when they look at bounding boxes and the grid, but improvements in search task performance could not be confirmed.
AbstractList We investigate four different variants of attention‐guiding video visualization techniques that aim to help users distribute their attention equally among potential objects of interest: bounding box visualization, force‐directed visualization, top‐down visualization, grid visualization. Objects of interest are highlighted by rectangular shapes and then we concentrate on the manipulation of color, motion, and size. We conducted a controlled laboratory user study (n=25) to compare the four visualization techniques and the unmodified video material as baseline. We evaluated task performance and distribution of attention in a search task. These two properties become especially important when video material with numerous objects has to be observed. The distribution of attention was measured by eye tracking. Our results show that a more even distribution of attention between the objects can be achieved by attention‐guiding visualization, compared to unmodified video. Many participants feel more comfortable when they look at bounding boxes and the grid, but improvements in search task performance could not be confirmed.
We investigate four different variants of attention-guiding video visualization techniques that aim to help users distribute their attention equally among potential objects of interest: bounding box visualization, force-directed visualization, top-down visualization, grid visualization. Objects of interest are highlighted by rectangular shapes and then we concentrate on the manipulation of color, motion, and size. We conducted a controlled laboratory user study (n=25) to compare the four visualization techniques and the unmodified video material as baseline. We evaluated task performance and distribution of attention in a search task. These two properties become especially important when video material with numerous objects has to be observed. The distribution of attention was measured by eye tracking. Our results show that a more even distribution of attention between the objects can be achieved by attention-guiding visualization, compared to unmodified video. Many participants feel more comfortable when they look at bounding boxes and the grid, but improvements in search task performance could not be confirmed [PUBLICATION ABSTRACT].
Author Kurzhals, K.
Weiskopf, D.
Höferlin, M.
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  organization: Visualization Research Center (VISUS), University of Stuttgart
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References_xml – reference: Fruchterman T. , Reingold E. : Graph drawing by force-directed placement . Software: Practice and Experience 21 , 11 ( 1991 ), 1129 - 1164 . 2 .
– reference: Wolfe J. , Yu K. , Stewart M. , Shorter A. , Friedman-Hill S. , Cave K. : Limitations on the parallel guidance of visual search: Color × color and orientation × orientation conjunctions . Journal of Experimental Psychology: Human Perception and Performance 16 , 4 ( 1990 ), 879 - 892 . 4 .
– reference: Wolfe J. , Reinecke A. , Brawn P. : Why don't we see changes? The role of attentional bottlenecks and limited visual memory . Visual Cognition 14 , 4-8 ( 2006 ), 749 - 780 . 1 .
– reference: Rensink R. A. : When good observers go bad: Change blindness, inattentional blindness, and visual experience . Psyche 6 , 9 ( 2000 ). 1 .
– reference: Kim Y. , Varshney A. : Saliency-guided enhancement for volume visualization . IEEE Transactions on Visualization and Computer Graphics 12 , 5 ( 2006 ), 925 - 932 . 2 .
– reference: Nilsson F. : Intelligent Network Video: Understanding Modern Video Surveillance Systems . CRC Press , 2009 . 1 .
– reference: Stevens S. : Psychophysics: Introduction to its Perceptual, Neural, and Social Prospects . Transaction Publishers , 1975 . 6 .
– reference: Chinchor N. , Thomas J. , Wong P. , Christel M. , Ribarsky W. : Multimedia analysis + visual analytics = multimedia analytics . IEEE Computer Graphics and Applications 30 , 5 ( 2010 ), 52 - 60 . 1 .
– reference: Henderson J. , Hollingworth A. : High-level scene perception . Annual Review of Psychology 50 , 1 ( 1999 ), 243 - 271 . 1 .
– reference: Biocca F. , Owen C. , Tang A. , Bohil C. : Attention issues in spatial information systems: directing mobile users' visual attention using augmented reality . Journal of Management Information Systems 23 , 4 ( 2007 ), 163 - 184 . 2 .
– reference: Grimes J. : On the failure to detect changes in scenes across saccades . Vancouver Studies in Cognitive Science 5 ( 1996 ), 89 - 110 . 2 .
– reference: Höferlin M. , Kurzhals K. , Höferlin B. , Heidemann G. , Weiskopf D. : Evaluation of fast-forward video visualization . IEEE Transactions on Visualization and Computer Graphics 18 , 12 ( 2012 ), 2095 - 2103 . 5 .
– reference: Keim D. , Mansmann F. , Schneidewind J. , Thomas J. , Ziegler H. : Visual analytics: Scope and challenges . Visual Data Mining 1 ( 2008 ), 76 - 90 . 1 .
– reference: Hillaire S. , Lecuyer A. , Regia-Corte T. , Cozot R. , Royan J. , Breton G. : Design and application of real-time visual attention model for the exploration of 3d virtual environments . IEEE Transactions on Visualization and Computer Graphics 18 , 3 ( 2012 ), 356 - 368 . 2 .
– reference: Wolfe J. , Horowitz T. : What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience 5 , 6 ( 2004 ), 495 - 501 . 2 .
– reference: Borgo R. , Chen M. , Daubney B. , Grundy E. , Heidemann G. , Höferlin B. , Höferlin M. , Leitte H. , Weiskopf D. , Xie X. : State of the art report on video-based graphics and video visualization . Computer Graphics Forum 31 , 8 ( 2012 ), 2450 - 2477 . 3 .
– reference: Healey C. , Enns J. : Attention and visual memory in visualization and computer graphics . IEEE Transactions on Visualization and Computer Graphics 18 , 7 ( 2012 ), 1170 - 1188 . 1 .
– reference: Höferlin M. , Höferlin B. , Weiskopf D. , Heidemann G. : Uncertainty-aware video visual analytics of tracked moving objects . Journal of Spatial Information Science 2 ( 2011 ), 87 - 117 . 3 .
– reference: Ripley B. : The R project in statistical computing . MSOR Connections. The Newsletter of the LTSN Maths, Stats & OR Network 1 , 1 ( 2001 ), 23 - 25 . 6 .
– reference: Harrower M. , Brewer C. : Colorbrewer.%20org: An online tool for selecting colour schemes for maps . The Cartographic Journal 40 , 1 ( 2003 ), 27 - 37 . 4 .
– reference: Wolfe J. , Butcher S. , Lee C. , Hyle M. : Changing your mind: on the contributions of top-down and bottom-up guidance in visual search for feature singletons . Journal of Experimental Psychology: Human Perception and Performance 29 , 2 ( 2003 ), 483 - 502 . 2 .
– reference: Cox D. , Reid N. : The Theory of the Design of Experiments . Chapman & Hall/CRC , 2000 . 5 .
– reference: Tatler B. , Wade N. , Kwan H. , Findlay J. , Velichkovsky B. : Yarbus, eye movements, and vision . i-Perception 1 , 1 ( 2010 ), 7 - 27 . 5 .
– reference: Andrienko G. , Andrienko N. , Burch M. , Weiskopf D. : Visual analytics methodology for eye movement studies . IEEE Transactions on Visualization and Computer Graphics 18 , 12 ( 2012 ), 2889 - 2898 . 2 .
– reference: Kosara R. , Healey C. , Interrante V. , Laidlaw D. , Ware C. : Thoughts on user studies: Why, how, and when . IEEE Computer Graphics and Applications 23 , 4 ( 2003 ), 20 - 25 . 2 .
– reference: Poole A. , Ball L. : Eye tracking in HCI and usability research . Encyclopedia of Human Computer Interaction 1 ( 2006 ), 211 - 219 . 2 .
– reference: Lansdowne Z. , Woodward B. : Applying the Borda ranking method . Air Force Journal of Logistics 20 , 2 ( 1996 ), 27 - 29 . 8 .
– reference: Cavanagh P. , Alvarez G. : Tracking multiple targets with multifocal attention . Trends in Cognitive Sciences 9 , 7 ( 2005 ), 349 - 354 . 1 .
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  year: 2007
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Snippet We investigate four different variants of attention‐guiding video visualization techniques that aim to help users distribute their attention equally among...
We investigate four different variants of attention-guiding video visualization techniques that aim to help users distribute their attention equally among...
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SubjectTerms Analysis
Colors (materials)
Computer graphics
Digital video
H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems-Evaluation/methodology
I.3.3 [Computer Graphics]: Picture/Image Generation-Display algorithms
I.3.m [Computer Graphics]: Miscellaneous-Video visualization
Performance evaluation
Searching
Stress concentration
Studies
Tasks
Tracking
Visualization
Title Evaluation of Attention-Guiding Video Visualization
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Volume 32
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