3D Layout Propagation to Improve Object Recognition in Egocentric Videos

Abstract

Intelligent systems need complex and detailed models of their environment to achieve more sophisticated tasks, such as assistance to the user. Vision sensors provide rich information and are broadly used to obtain these models, for example, indoor scene modeling from monocular images has been widely studied. A common initial step in those settings is the estimation of the $3$ D layout of the scene. While most of the previous approaches obtain the scene layout from a single image, this work presents a novel approach to estimate the initial layout and addresses the problem of how to propagate it on a video. We propose to use a particle filter framework for this propagation process and describe how to generate and sample new layout hypotheses for the scene on each of the following frames. We present different ways to evaluate and rank these hypotheses. The experimental validation is run on two recent and publicly available datasets and shows promising results on the estimation of a basic $3$ D layout. Our experiments demonstrate how this layout information can be used to improve detection tasks useful for a human user, in particular sign detection, by easily rejecting false positives.