Mode-of-disparities error correction of eye-tracking data

Zhang, Yunfeng; Hornof, Anthony J.

doi:10.3758/s13428-011-0073-0

Cited by 32 publications

(39 citation statements)

References 8 publications

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“…Komogortsev and Khan (2008), for example, used an eyetracker with an accuracy specification of 0.5°but found that, after removing all invalid recordings, the average accuracy over participants was 1.0°. Zhang and Hornof (2011), Hansen and Ji (2009), and others have reported similar results. Furthermore, accuracy depends strongly on the particular characteristics of the individual participant (Hornof & Halverson, 2002), with head movements, astigmatism, and eyelid closure being particularly troublesome factors that can cause inaccuracies of several degrees of visual angle and can be detrimental to position-based data analysis and interaction.…”

supporting

confidence: 68%

The influence of calibration method and eye physiology on eyetracking data quality

et al. 2012

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Recording eye movement data with high quality is often a prerequisite for producing valid and replicable results and for drawing well-founded conclusions about the oculomotor system. Today, many aspects of data quality are often informally discussed among researchers but are very seldom measured, quantified, and reported. Here we systematically investigated how the calibration method, aspects of participants' eye physiologies, the influences of recording time and gaze direction, and the experience of operators affect the quality of data recorded with a common tower-mounted, video-based eyetracker. We quantified accuracy, precision, and the amount of valid data, and found an increase in data quality when the participant indicated that he or she was looking at a calibration target, as compared to leaving this decision to the operator or the eyetracker software. Moreover, our results provide statistical evidence of how factors such as glasses, contact lenses, eye color, eyelashes, and mascara influence data quality. This method and the results provide eye movement researchers with an understanding of what is required to record high-quality data, as well as providing manufacturers with the knowledge to build better eyetrackers.

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confidence: 68%

The influence of calibration method and eye physiology on eyetracking data quality

et al. 2012

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“…The technique uses RFLs [Hornof and Halverson, 2002] and PFLs [Zhang and Hornof, 2011] to measure eye tracking error, and uses quadratic equations to characterize the change in error across the display. Example data from an eye tracking experiment demonstrate that eye tracking error that initially appears to be somewhat random can actually follow a smooth quadratic curve.…”

Section: Resultsmentioning

confidence: 99%

“…Because the number was small (0.26º in height), the participants had to look directly at the numbers to find the target. Similar to Zhang and Hornof [2011], fixations in this condition were mapped to their nearest PFLs. As mentioned earlier, the RFLs were given a larger weight than the PFLs in the robust linear regression computation.…”

Section: Validation Of the Error Correction Methodsmentioning

confidence: 99%

“…For example, small text that needs to be read at an arbitrary point in a task could be used as a PFL because the participant would need to look directly at the text to recognize it (whereas other visual properties such as color could be recognized in the periphery), but the participant could do so at various different times in a trial, which would make it difficult to determine which exact fixation should be mapped to the PFL. Zhang and Hornof's [2011] solution to this problem was to map each fixation to its nearest PFL. The authors showed that with an noise-resilient algorithm (in their case, the somewhat complicated mean-shift algorithm), the method will reliably identify the pattern of error provided that the RFLs are not arranged in an evenly-spaced grid pattern and that the error is constant across the display.…”

Section: Error Correction By Spatial Recalibrationmentioning

confidence: 99%

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Easy post-hoc spatial recalibration of eye tracking data

Zhang

Hornof

2014

Proceedings of the Symposium on Eye Tracking Research and Applications

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The gaze locations reported by eye trackers often contain error resulting from a variety of sources. Such error is of increasing concern to eye tracking researchers, and several techniques have been introduced to clean up the error. These methods, however, either compensate only for error caused by a particular source (such as pupil dilation) or require the error to be somewhat constant across space and time. This paper introduces a method that is applicable to error generated from a variety of sources and that is resilient to the change in error across the display. A study shows that, at least in some cases, although the change in error across the display appears to be random it in fact follows a consistent pattern which can be modeled using quadratic equations. The parameters of these equations can be estimated using linear regression on the error vectors between recorded fixations and possible target locations. The resulting equations can then be used to clean up the error. This regression-based approach is much easier to apply than some of the previously published methods. The method is applied to the data of a visual search experiment, and the results show that the regression-based error correction works very well.

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“…This problem is increased if the analysis has to be rerun, in case for example some objects (e.g., windows) are to be removed from the analysis or the calibration of the eye tracker had a small divergent. The online matching of dynamic AOIs also has the disadvantage of not applying any method for correcting the eye-movement data (Hornof & Halverson, 2002;Zhang & Hornof, 2011). Due to the research presented above, the authors believe that, at present, methods of replay and post correction are necessary in the field of eyemovement analysis.…”

Section: Existing Approachesmentioning

confidence: 99%

A guideline for integrating dynamic areas of interests in existing set-up for capturing eye movement: Looking at moving aircraft

2016

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Today, capturing the behavior of a human eye is considered a standard method for measuring the information-gathering process and thereby gaining insights into cognitive processes. Due to the dynamic character of most task environments there is still a lack of a structured and automated approach for analyzing eye movement in combination with moving objects. In this article, we present a guideline for advanced gaze analysis, called IGDAI (Integration Guideline for Dynamic Areas of Interest). The application of IGDAI allows gathering dynamic areas of interest and simplifies its combination with eye movement. The first step of IGDAI defines the basic requirements for the experimental setup including the embedding of an eye tracker. The second step covers the issue of storing the information of task environments for the dynamic AOI analysis. Implementation examples in XML are presented fulfilling the requirements for most dynamic task environments. The last step includes algorithms to combine the captured eye movement and the dynamic areas of interest. A verification study was conducted, presenting an air traffic controller environment to participants. The participants had to distinguish between different types of dynamic objects. The results show that in comparison to static areas of interest, IGDAI allows a faster and more detailed view on the distribution of eye movement.

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Mode-of-disparities error correction of eye-tracking data

Cited by 32 publications

References 8 publications

The influence of calibration method and eye physiology on eyetracking data quality

The influence of calibration method and eye physiology on eyetracking data quality

Easy post-hoc spatial recalibration of eye tracking data

A guideline for integrating dynamic areas of interests in existing set-up for capturing eye movement: Looking at moving aircraft

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