Deepdive: a learning-based approach for virtual camera in immersive contents

Q1 Computer Science
Muhammad Irfan , Muhammad Munsif
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引用次数: 2

Abstract

A 360° video stream provide users a choice of viewing one's own point of interest inside the immersive contents. Performing head or hand manipulations to view the interesting scene in a 360° video is very tedious and the user may view the interested frame during his head/hand movement or even lose it. While automatically extracting user's point of interest (UPI) in a 360° video is very challenging because of subjectivity and difference of comforts. To handle these challenges and provide user's the best and visually pleasant view, we propose an automatic approach by utilizing two CNN models: object detector and aesthetic score of the scene. The proposed framework is three folded: pre-processing, Deepdive architecture, and view selection pipeline. In first fold, an input 360° video-frame is divided into three subframes, each one with 120° view. In second fold, each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score. Finally, decision pipeline selects the subframe with salient object based on the detected object and calculated aesthetic score. As compared to other state-of-the-art techniques which are domain specific approaches i.e., support sports 360° video, our system support most of the 360° videos genre. Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360° videos.

深度潜水:一种基于学习的沉浸式内容虚拟相机方法
360°视频流为用户提供了在沉浸式内容中观看自己感兴趣的点的选择。在360°视频中执行头部或手部操作来查看有趣的场景是非常繁琐的,用户可能会在他的头部/手部运动中查看感兴趣的帧,甚至失去它。而在360°视频中自动提取用户兴趣点(UPI)由于主观性和舒适度的差异是非常具有挑战性的。为了应对这些挑战并为用户提供最佳的视觉愉悦视图,我们提出了一种利用两个CNN模型的自动方法:对象检测器和场景美学评分。该框架分为三个部分:预处理、Deepdive架构和视图选择管道。在第一次折叠中,输入的360°视频帧被分成三个子帧,每个子帧具有120°视图。在第二步中,每个子帧通过CNN模型提取子帧中的视觉特征并计算美学分数。最后,决策流水线根据检测到的目标和计算出的美学分数,选择具有显著目标的子框架。与其他最先进的技术相比,这些技术是特定领域的方法,即支持体育360°视频,我们的系统支持大多数360°视频类型。根据我们自己从各个网站收集的数据对提出的框架进行性能评估,表明不同类别的360°视频的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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