Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126651
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Type: Conference paper
Title: Real-time human gaze estimation
Author: Rowntree, T.
Pontecorvo, C.
Reid, I.
Citation: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2019), 2019 / pp.1-7
Publisher: IEEE
Publisher Place: online
Issue Date: 2019
ISBN: 9781728138572
Conference Name: International Conference on Digital Image Computing: Techniques and Applications (DICTA) (02 Dec 2019 - 04 Dec 2019 : Perth, Australia)
Statement of
Responsibility: 
Thomas Rowntree, Carmine Pontecorvo, Ian Reid
Abstract: This paper describes a system for estimating the course gaze or 1D head pose of multiple people in a video stream from a moving camera in an indoor scene. The system runs at 30 Hz and can detect human heads with a F-Score of 87.2% and predict their gaze with an average error 20.9° including when they are facing directly away from the camera. The system uses two Convolutional Neural Networks (CNNs) for head detection and gaze estimation respectively and uses common tracking and filtering techniques for smoothing predictions over time. This paper is application-focused and so describes the individual components of the system as well as the techniques used for collecting data and training the CNNs.
Rights: © 2019 IEEE
RMID: 1000014158
DOI: 10.1109/DICTA47822.2019.8945919
Published version: https://ieeexplore.ieee.org/xpl/conhome/8943071/proceeding
Appears in Collections:Computer Science publications

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