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TRACK: A Multi-Modal Deep Architecture for Head Motion Prediction in 360-Degree Videos

Miguel Romero Rondon 1, 2 Lucile Sassatelli 1 Ramon Aparicio-Pardo 1 Frédéric Precioso 3, 2
2 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems, UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné
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.
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Contributor : Lucile Sassatelli <>
Submitted on : Thursday, July 23, 2020 - 7:45:11 PM
Last modification on : Monday, March 29, 2021 - 2:46:21 PM
Long-term archiving on: : Tuesday, December 1, 2020 - 6:29:20 AM


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  • HAL Id : hal-02615980, version 1



Miguel Romero Rondon, Lucile Sassatelli, Ramon Aparicio-Pardo, Frédéric Precioso. TRACK: A Multi-Modal Deep Architecture for Head Motion Prediction in 360-Degree Videos. ICIP 2020 - IEEE International Conference on Image Processing, Oct 2020, Abu Dhabi / Virtual, United Arab Emirates. ⟨hal-02615980⟩



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