3D teleimmersive activity classification based on application-system metadata

Aadhar Jain, A. Arefin, Raoul Rivas, Chien-Nan Chen, K. Nahrstedt
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引用次数: 6

Abstract

Being able to detect and recognize human activities is essential for 3D collaborative applications for efficient quality of service provisioning and device management. A broad range of research has been devoted to analyze media data to identify human activity, which requires the knowledge of data format, application-specific coding technique and computationally expensive image analysis. In this paper, we propose a human activity detection technique based on application generated metadata and related system metadata. Our approach does not depend on specific data format or coding technique. We evaluate our algorithm with different cyber-physical setups, and show that we can achieve very high accuracy (above 97%) by using a good learning model.
基于应用系统元数据的三维远程沉浸式活动分类
能够检测和识别人类活动对于3D协作应用程序至关重要,以实现高效的服务提供和设备管理质量。广泛的研究致力于分析媒体数据以识别人类活动,这需要数据格式的知识,特定应用的编码技术和计算昂贵的图像分析。本文提出了一种基于应用生成元数据和相关系统元数据的人类活动检测技术。我们的方法不依赖于特定的数据格式或编码技术。我们用不同的网络物理设置评估了我们的算法,并表明通过使用良好的学习模型,我们可以达到非常高的准确率(97%以上)。
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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