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Title: Predicting head trajectories in 360° virtual reality videos
Authors: Aladagli, A. Deniz
Ekmekcioglu, Erhan
Jarnikov, Dmitri
Kondoz, Ahmet
Keywords: 360
VR
Video
Omnidirectional
Head
Prediction
Saliency
Issue Date: 2018
Publisher: © Institute of Electrical and Electronics Engineers (IEEE)
Citation: ALADAGLI, A.D. ...et al., 2018. Predicting head trajectories in 360° virtual reality videos. Presented at the 2017 International Conference on 3D Immersion (IC3D), Brussels, Belgium, 11-12 Dec. 2017.
Abstract: In this paper a fixation prediction based saliency algorithm is used in order to predict the head movements of viewers watching virtual reality (VR) videos, by modelling the relationship between fixation predictions and recorded head movements. The saliency algorithm is applied to viewings faithfully recreated from recorded head movements. Spherical cross-correlation analysis is performed between predicted attention centres and actual viewing centres in order to try and identify prevalent lengths of predictable attention and how early they can be predicted. The results show that fixation prediction based saliency analysis correlates with head movements only for limited durations. Therefore, further classification of durations where saliency analysis is predictive is required.
Description: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: The work presented in this paper was carried out as part of CLOUDSCREENS, a Marie Curie Initial Training Networks action funded by the European Commissions 7th Framework Program under the Grant Number 608028.
Version: Accepted for publication
DOI: 10.1109/IC3D.2017.8251913
URI: https://dspace.lboro.ac.uk/2134/32375
Publisher Link: https://doi.org/10.1109/IC3D.2017.8251913
ISBN: 9781538646557
Appears in Collections:Conference Papers and Presentations (Loughborough University London)

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