Name
Title | ||
|---|---|---|
Real-time tracking of visually attended objects in virtual environments and its application to LOD. |
Abstract | ||
|---|---|---|
This paper presents a real-time framework for computationally tracking objects visually attended by the user while navigating in interactive virtual environments. In addition to the conventional bottom-up (stimulus-driven) saliency map, the proposed framework uses top-down (goal-directed) contexts inferred from the user's spatial and temporal behaviors, and identifies the most plausibly attended objects among candidates in the object saliency map. The computational framework was implemented using GPU, exhibiting high computational performance adequate for interactive virtual environments. A user experiment was also conducted to evaluate the prediction accuracy of the tracking framework by comparing objects regarded as visually attended by the framework to actual human gaze collected with an eye tracker. The results indicated that the accuracy was in the level well supported by the theory of human cognition for visually identifying single and multiple attentive targets, especially owing to the addition of top-down contextual information. Finally, we demonstrate how the visual attention tracking framework can be applied to managing the level of details in virtual environments, without any hardware for head or eye tracking. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TVCG.2008.82 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
interactive virtual environment,virtual environments,user experiment,gpu,computationally tracking object,virtual reality,proposed framework,virtual environment,computational framework,target tracking,tracking framework,eye tracker,visual attention tracking framework,bottom-up saliency map,real-time framework,eye tracking,human cognition theory,cognitive simulation,object recognition,top-down contextual information,visually attended objects,real-time tracking,level of detail,top down,user experience,real time,navigation,accuracy,visual perception,video compression,human cognition,bottom up,high performance computing | Computer vision,Virtual reality,Virtual machine,Level of detail,Computer science,Eye tracking,Artificial intelligence,Data compression,OpenGL,Visual perception,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
15 | 1 | 1077-2626 |
Citations | PageRank | References |
24 | 1.07 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sungkil Lee | 1 | 226 | 21.93 |
| Gerard Jounghyun Kim | 2 | 571 | 51.97 |
| Seungmoon Choi | 3 | 921 | 105.64 |