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• W3150389638 abstract "We consider predicting the user's head motion in 360 ° videos, with 2 modalities only: the past user's positions and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning approaches for this problem and identify hidden flaws from a thorough root-cause analysis. Second, from the results of this analysis, we design a new proposal establishing state-of-the-art performance. First, re-assessing the existing methods that use both modalities, we obtain the surprising result that they all perform worse than baselines using the user's trajectory only. A root-cause analysis of the metrics, datasets and neural architectures shows in particular that (i) the content can inform the prediction for horizons longer than 2 to 3 sec. (existing methods consider shorter horizons), and that (ii) to compete with the baselines, it is necessary to have a recurrent unit dedicated to process the positions, but this is not sufficient. Second, from a re-examination of the problem supported with the concept of Structural-RNN, we design a new deep neural architecture, named TRACK. TRACK achieves state-of-the-art performance on all considered datasets and prediction horizons, outperforming competitors by up to 20 percent on focus-type videos and horizons 2-5 seconds. The entire framework (codes and datasets) is online and received an ACM reproducibility badge https://gitlab.com/miguelfromeror/head-motion-prediction." @default.
• W3150389638 created "2021-04-13" @default.
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• W3150389638 date "2021-01-01" @default.
• W3150389638 modified "2023-10-09" @default.
• W3150389638 title "TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos" @default.
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• W3150389638 doi "https://doi.org/10.1109/tpami.2021.3070520" @default.
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