User:Mikejzel/sandbox

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THIS IS A DRAFTING SPACE. ALL NECESSARY PROCEDURES TO PROPERLY INSERT THIS INFORMATION TO THE RELEVANT ARTICLE WILL BE TAKEN AT A LATER DATE


To do re: Eye Tracking

-Insert images into AI section

Namely, the image on the right to illustrate the structure of a convolutional neural network


CNN architecture and computational flow

-Add AI section


-Include studies in "Practice" section

#1 candidate: Driver Drowsiness from Zhou et al.

-Elaborate on use of AI in data presentation, seen below

Data presentation[edit]

Interpretation of the data that is recorded by the various types of eye-trackers employs a variety of software that animates or visually represents it, so that the visual behavior of one or more users can be graphically resumed. The video is generally manually coded to identify the AOIs(Area Of Interests) or recently using artificial intelligence. Graphical presentation is rarely the basis of research results, since they are limited in terms of what can be analysed - research relying on eye-tracking, for example, usually requires quantitative measures of the eye movement events and their parameters, The following visualisations are the most commonly used:

Animated representations of a point on the interface This method is used when the visual behavior is examined individually indicating where the user focused their gaze in each moment, complemented with a small path that indicates the previous saccade movements, as seen in the image.

Static representations of the saccade path This is fairly similar to the one described above, with the difference that this is static method. A higher level of expertise than with the animated ones is required to interpret this.

Heat maps An alternative static representation, used mainly for the agglomerated analysis of the visual exploration patterns in a group of users. In these representations, the ‘hot’ zones or zones with higher density designate where the users focused their gaze (not their attention) with a higher frequency. Heat maps are the best known visualization technique for eyetracking studies.

Blind zones maps, or focus maps This method is a simplified version of the Heat maps where the visually less attended zones by the users are displayed clearly, thus allowing for an easier understanding of the most relevant information, that is to say, we are informed about which zones were not seen by the users.

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Saliency maps Similar to heat maps, a saliency map illustrates areas of focus by brightly displaying the attention-grabbing objects over an initially black canvas. The more focus is given to an object, the brighter it appears.

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Likely Works Cited:

[1] J. Le, Thomas, Crowley, James, and Dominique, “Deep learning investigation for chess player attention prediction using eye-tracking and game data,” arXiv.org, 17-Apr-2019. [Online]. Available: https://arxiv.org/abs/1904.08155. [Accessed: 20-Oct-2019].

[2] Zhao, Lei; Wang, Zengcai; Zhang, Guoxin; Qi, Yazhou; Wang, Xiaojin

ISSN: 1380-7501 , 1573-7721; DOI: 10.1007/s11042-017-5380-8

Multimedia tools and applications , 2018, Vol.77(15), p.19415-19438