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Scene Semantics from Long-Term Observation of People
Vincent Delaitre1, David F. Fouhey2, Ivan Laptev1, Josef Sivic1, Abhinav Gupta2, and Alexei A. Efros1, 2
1INRIA/École Normale Supérieure, Paris, France
2Carnegie Mellon University, USA
Abstract. Our everyday objects support various tasks and can be used by people for different purposes. While object classification is a widely studied topic in computer vision, recognition of object function, i.e., what people can do with an object and how they do it, is rarely addressed. In this paper we construct a functional object description with the aim to recognize objects by the way people interact with them. We describe scene objects (sofas, tables, chairs) by associated human poses and object appearance. Our model is learned discriminatively from automatically estimated body poses in many realistic scenes. In particular, we make use of time-lapse videos from YouTube providing a rich source of common human-object interactions and minimizing the effort of manual object annotation. We show how the models learned from human observations significantly improve object recognition and enable prediction of characteristic human poses in new scenes. Results are shown on a dataset of more than 400,000 frames obtained from 146 time-lapse videos of challenging and realistic indoor scenes.
LNCS 7577, p. 284 ff.
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© Springer-Verlag Berlin Heidelberg 2012