Indoor Scene Recognition Through Object Detection

Indoor Scene Recognition Through Object Detection. Espinace, P., Kollar, T., Soto, A., & Roy, N. In Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA), 2010.

Paper abstract bibtex 2 downloads

Scene recognition is a highly valuable percep- tual 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 high- level 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 low- level 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-of- the-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.

@InProceedings{	  espinace:etal:2010,
  author	= {P. Espinace and T. Kollar and A. Soto and N. Roy},
  title		= {Indoor Scene Recognition Through Object Detection},
  booktitle	= {Proc. of IEEE Int. Conf. on Robotics and Automation
		  (ICRA)},
  year		= {2010},
  abstract	= {Scene recognition is a highly valuable percep- tual
		  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 high- level 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 low- level
		  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-of- the-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. },
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/Icra-2010.pdf}
}

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