Track: a Multi-Modal Deep Architecture for Head Motion Prediction in 360° Videos

Miguel Fabián Romero Rondón, Lucile Sassatelli, Ramon Aparicio-Pardo, Frédéric Precioso

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.