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A multicue Bayesian state estimator for gaze prediction in open signed video

Research output: Contribution to journalArticle

Original languageEnglish
Pages39 - 48
Number of pages10
JournalIEEE Transactions on Multimedia
Journal publication dateJan 2009
Volume11
Journal issue1
DOIs
StatePublished

Abstract

We propose a multicue gaze prediction framework for open signed video content, the benefits of which include coding gains without loss of perceived quality. We investigate which cues are relevant for gaze prediction and find that shot changes, facial orientation of the signer and face locations are the most useful. We then design a face orientation tracker based upon grid-based likelihood ratio trackers, using profile and frontal face detections. These cues are combined using a grid-based Bayesian state estimation algorithm to form a probability surface for each frame. We find that this gaze predictor outperforms a static gaze prediction and one based on face locations within the frame.

Additional information

Publisher: IEEE Rose publication type: Journal article Sponsorship: The work of SJC Davies was supported by the British Broadcasting Corporation (BBC). Terms of use: Copyright © 2009 IEEE. Reprinted from IEEE Transactions on Multimedia. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

Research areas

  • eye-tracking, face detection, gaze prediction, video coding

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