Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Building a lightweight eyetracking headgear
Proceedings of the 2004 symposium on Eye tracking research & applications
Causal saliency effects during natural vision
Proceedings of the 2006 symposium on Eye tracking research & applications
Computational mechanisms for gaze direction in interactive visual environments
Proceedings of the 2006 symposium on Eye tracking research & applications
Where people look when watching movies: Do all viewers look at the same place?
Computers in Biology and Medicine
ACM Transactions on Applied Perception (TAP)
Qualitative and quantitative scoring and evaluation of the eye movement classification algorithms
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Match-moving for area-based analysis of eye movements in natural tasks
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Multi-mode saliency dynamics model for analyzing gaze and attention
Proceedings of the Symposium on Eye Tracking Research and Applications
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Video-based eye trackers produce an output video showing where a subject is looking, the subject's point-of-regard (POR), for each frame of a video of the scene. Fixation-identification algorithms simplify the long list of POR data into a more manageable set of data, especially for further analysis, by grouping PORs into fixations. Most current fixation-identification algorithms assume that the POR data are defined in static two-dimensional scene images and only use these raw POR data to identify fixations. The applicability of these algorithms to gaze data in dynamic scene videos is largely unexplored. We implemented a simple velocity-based, duration-sensitive fixation-identification algorithm and compared its performance to results obtained by three experienced users manually coding the eye tracking data displayed within the scene video such that these manual coders had knowledge of the scene motion. We performed this comparison for eye tracking data collected during two different tasks involving different types of scene motion. These two tasks included a subject walking around a building for about 100 seconds (Task 1) and a seated subject viewing a computer animation (approximately 90 seconds long, Task 2). It took our manual coders on average 75 minutes (stdev = 28) and 80 minutes (17) to code results from the first and second tasks, respectively. The automatic fixation-identification algorithm, implemented in MATLAB and run on an Apple 2.16 GHz MacBook, produced results in 0.26 seconds for Task 1 and 0.21 seconds for Task 2. For the first task (walking), the average percent difference among the three human manual coders was 9% (3.5) and the average percent difference between the automatically generated results and the three coders was 11% (2.0). For the second task (animation), the average percent difference among the three human coders was 4% (0.75) and the average percent difference between the automatically generated results and the three coders was 5% (0.9).