What Are You Looking at? Improving Visual Gaze Estimation by Saliency

In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find...

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Bibliographic Details
Published inInternational journal of computer vision Vol. 98; no. 3; pp. 324 - 334
Main Authors Valenti, Roberto, Sebe, Nicu, Gevers, Theo
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.07.2012
Springer
Subjects
Accuracy
Applied sciences
Artificial Intelligence
Computer Imaging
Computer Science
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Correlation
Exact sciences and technology
Image Processing and Computer Vision
Mathematical analysis
Pattern Recognition
Pattern Recognition and Graphics
Pattern recognition. Digital image processing. Computational geometry
Software
Tracking
Vectors (mathematics)
Vision
Visual
Webcams
Saliency
Gaze estimation
Eye location
HCI
Head pose
Computer vision
Probabilistic approach
Gaze
Pursuit eye movement
Posture
Visual system
User interface
User assistance
Localization
Relief
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Summary:In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-011-0511-6