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TRACK: A Multi-Modal Deep Architecture for Head Motion Prediction in 360-Degree Videos
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.
Cited literature [19 references]
https://hal.archives-ouvertes.fr/hal-02615980
Contributor : Lucile Sassatelli Connect in order to contact the contributor
Submitted on : Thursday, July 23, 2020 - 7:45:11 PM
Last modification on : Sunday, May 1, 2022 - 3:16:57 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 6:29:20 AM
Contributor : Lucile Sassatelli Connect in order to contact the contributor
Submitted on : Thursday, July 23, 2020 - 7:45:11 PM
Last modification on : Sunday, May 1, 2022 - 3:16:57 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 6:29:20 AM
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ICIP2020_accepted.pdf
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