基于RGB-D数据和2.5D cnn的场景理解和家用机器人自动风险评估

Rob Dupre, Georgios Tzimiropoulos, V. Argyriou
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引用次数: 2

摘要

在这项工作中,解决了3D场景自动风险评估的概念。使用深度学习技术,智能家庭和家用机器人可以配备检测、引起注意或减轻给定场景中的危险的功能。我们通过引入一种新颖的CNN架构,扩展了现有的风险估计框架,该框架结合了物理和形状描述符,允许在补丁级别进行风险检测。对RGB-D数据进行分析,并以帧为单位进行分析,帧之间不需要时间信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level
In this work the notion of automated risk assessment for 3D scenes is addressed. Using deep learning techniques smart enabled homes and domestic robots can be equipped with the functionality to detect, draw attention to, or mitigate hazards in a given scene. We extend an existing risk estimation framework that incorporates physics and shape descriptors by introducing a novel CNN architecture allowing risk detection at a patch level. Analysis is conducted on RGB-D data and is performed on a frame by frame basis, requiring no temporal information between frames.
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