Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/87247
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Type: Conference paper
Title: Unsupervised learning of a scene-specific coarse gaze estimator
Author: Benfold, B.
Reid, I.
Citation: 2011 IEEE International Conference on Computer Vision, 2011 / pp.2344-2351
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781457711015
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (ICCV) (06 Nov 2011 - 13 Nov 2011 : Barcelona, Spain)
Statement of
Responsibility: 
Ben Benfold and Ian Reid
Abstract: We present a method to estimate the coarse gaze directions of people from surveillance data. Unlike previous work we aim to do this without recourse to a large hand-labelled corpus of training data. In contrast we propose a method for learning a classifier without any hand labelled data using only the output from an automatic tracking system. A Conditional Random Field is used to model the interactions between the head motion, walking direction, and appearance to recover the gaze directions and simultaneously train randomised decision tree classifiers. Experiments demonstrate performance exceeding that of conventionally trained classifiers on two large surveillance datasets.
Rights: ©2011 IEEE
RMID: 0020131154
DOI: 10.1109/ICCV.2011.6126516
Appears in Collections:Computer Science publications

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