Towards automated comparison of eye-tracking recordings in dynamic scenes

Kübler, Thomas C.; Bukenberger, Dennis R.; Ungewiß, Judith; Worner, Alexandra; Rothe, Colleen; Schiefer, Ulrich; Kasneci, Enkelejda

doi:10.1109/euvip.2014.7018371

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…There have been approaches to semi-automatically determine interesting objects and to track them Kübler et al (2014a), however they are not generically applicable. We already mentioned approaches to cluster fixation locations in order to determine ROIs from the data (Heminghous and Duchowski, 2006).…”

Section: String Conversionmentioning

confidence: 99%

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

et al. 2016

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Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: www.ti.uni-tuebingen.de/scanpathcomparison.html.

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Section: String Conversionmentioning

confidence: 99%

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

et al. 2016

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“…We want to extend the general and AOI based statistics calculations, add new calculation or visualization algorithms and make the existing ones more interactive and transparent. A special focus will be given the analysis and processing of saccadic eye movements as well as to the automated annotation of AOIs for dynamic scenarios (Kübler et al, 2014) and non-elliptical AOIs.…”

Section: Discussionmentioning

confidence: 99%

“…We plan on including further automated scanpath comparison metrics, such as MultiMatch (Dewhurst et al, 2012) or SubsMatch (Kübler et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Eyetrace2014 - Eyetracking Data Analysis Tool

Sippel

Kübler

Fuhl

et al. 2015

Proceedings of the International Conference on Health Informatics

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Over the last years eye tracking became more and more popular. A variety of new eye-tracker models and algorithms for eye tracking data processing emerged. On the one hand this multitude of hard-and software brought many advantages, on the other hand the diversity of devices and measures impedes the comparability and repeatability of eye-tracking studies. While supply of eye tracking software is high, the functioning of the algorithms, e.g. how fixations are identified, is often intransparent and unflexible. The Eyetrace software bundle approaches these problems by providing a variety of different evaluation methods compatible with many eye-tracker models. Eyetrace2014 combines state-of-the-art algorithms with established approaches and provides a continuous visualization of the analysis process. All calculations provide user adaptable parameters and are well documented and referenced in order to make the whole analysis transparent. Our software is available free of charge well suited for exploratory data analysis and education (http://www.ti.uni-tuebingen.de/Eyetrace.1751.0.html).

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“…Thumbnails of size 100 x 100 pixels around each eye gaze point are extracted from multiple video recordings and clustered accordingly. Automatic annotation using scale-invariant feature transform were also used to detect distinct features in videos [64]. Numerous other methods of labelling eye gaze samples based on the underlying context are discussed in [70,71,92,98].…”

Section: Data Abstraction -Symbolic Approximationmentioning

confidence: 99%