English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Video-based eye tracking is a valuable technique in various research fields.Numerous open-source eye tracking algorithms have been developed in recentyears, primarily designed for general application with many different cameratypes. These algorithms do not, however, capitalize on the high frame rate ofeye tracking cameras often employed in psychophysical studies. We present apupil detection method that utilizes this high-speed property to obtainreliable predictions through recursive estimation about certain pupilcharacteristics in successive camera frames. These predictions are subsequentlyused to carry out novel image segmentation and classification routines toimprove pupil detection performance. Based on results from hand-labelled eyeimages, our approach was found to have a greater detection rate, accuracy andspeed compared to other recently published open-source pupil detectionalgorithms. The program's source code, together with a graphical userinterface, can be downloaded at https://github.com/tbrouns/eyestalker

Robust Video-Based Eye Tracking Using Recursive Estimation of Pupil Characteristics