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Real-time non-intrusive eye tracking for human-computer interaction

Authors: Bai Baogang; Li Shaozi; Lin Kunhui; Wu Tunhua; Zhou Chang-le;

Real-time non-intrusive eye tracking for human-computer interaction

Abstract

A real-time eye tracking method was introduced. The method integrates several algorithms such as AdaBoost, optical flow, and Camshift. First, lighting condition was standardized, and the camera was placed below face. Second, the nostril tracking points were localized and taken as reference points. Third, the Camshift algorithm and Lucas-Kanade optical flow algorithm were respectively utilized to track the face and nostrils, and the pupils were positioned using gradient Hough circle transform. Finally, with the coordinates of nostrils and pupils, the velocity and trajectory of eye movement were calculated. The experiments on a video of resolution at 640⋆480 result a tracking speed of 25 frames per second. Results prove the effectiveness and efficiency of this method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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