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

Description

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.

Abstract

International audience

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