Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level

Abstract

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.