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- W3027786104 abstract "Head motion prediction is an important problem with 360° videos, in particular to inform the streaming decisions. Various methods tackling this problem with deep neural networks have been proposed recently. In this article, we introduce a new deep architecture, named TRACK, that benefits both from the history of past positions and knowledge of the video content. We show that TRACK achieves state-of-the-art performance when compared against all recent approaches considering the same datasets and wider prediction horizons: from 0 to 5 seconds." @default.
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- W3027786104 date "2020-10-01" @default.
- W3027786104 modified "2023-10-04" @default.
- W3027786104 title "Track: a Multi-Modal Deep Architecture for Head Motion Prediction in 360° Videos" @default.
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- W3027786104 doi "https://doi.org/10.1109/icip40778.2020.9191331" @default.
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