Unlike conventional videos, 360{\deg} videos give freedom to users to turn their heads, watch and interact with the content owing to its immersive spherical environment. Although these movements are arbitrary, similarities can be observed between viewport patterns of different users and different videos. Identifying such patterns can assist both content and network providers to enhance the 360{\deg} video streaming process, eventually increasing the end-user Quality of Experience (QoE). But a study on how viewport patterns display similarities across different video content, and their potential applications has not yet been done. In this paper, we present a comprehensive analysis of a dataset of 88 360{\deg} videos and propose a novel video categorization algorithm that is based on similarities of viewports. First, we propose a novel viewport clustering algorithm that outperforms the existing algorithms in terms of clustering viewports with similar positioning and speed. Next, we develop a novel and unique dynamic video segment categorization algorithm that shows notable improvement in similarity for viewport distributions within the clusters when compared to that of existing static video categorizations.

中文翻译：

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视口感知的动态360°视频片段分类

与传统视频不同，由于其身临其境的球形环境，360 {\ deg}视频使用户可以自由转动头，观看内容并与内容进行交互。尽管这些移动是任意的，但是可以在不同用户和不同视频的视口模式之间观察到相似之处。识别出这种模式可以帮助内容提供商和网络提供商增强360°视频流传输过程，最终提高最终用户的体验质量（QoE）。但是，关于视口模式如何在不同视频内容之间显示相似性及其潜在应用的研究尚未完成。在本文中，我们对88 360 {\ deg}个视频的数据集进行了全面分析，并提出了一种基于视口相似性的新颖视频分类算法。第一的，我们提出了一种新颖的视口聚类算法，该算法在具有相似定位和速度的聚类视口方面优于现有算法。接下来，我们开发一种新颖而独特的动态视频片段分类算法，与现有的静态视频分类方法相比，该算法在群集内的视口分布的相似性方面显示出显着的改进。