Scence Recognition Through Object Detection

Abstract

Scene recognition is a highly valuable perceptual
ability for an indoor mobile robot, however, current
approaches for scene recognition present a significant drop in
performance for the case of indoor scenes. We believe that
this can be explained by the high appearance variability of
indoor environments. This stresses the need to include highlevel
semantic information in the recognition process. In this
work we propose a new approach for indoor scene recognition
based on a generative probabilistic hierarchical model that uses
common objects as an intermediate semantic representation.
Under this model, we use object classifiers to associate lowlevel
visual features to objects, and at the same time, we use
contextual relations to associate objects to scenes. As a further
contribution, we improve the performance of current state-ofthe-
art category-level object classifiers by including geometrical
information obtained from a 3D range sensor that facilitates
the implementation of a focus of attention mechanism within a
Monte Carlo sampling scheme. We test our approach using real
data, showing significant advantages with respect to previous
state-of-the-art methods.