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Weakly Supervised Learning of Interactions between Humans and Objects

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3 Author(s)
Alessandro Prest ; ETH, Zurich, and INRIA, Grenoble ; Cordelia Schmid ; Vittorio Ferrari

We introduce a weakly supervised approach for learning human actions modeled as interactions between humans and objects. Our approach is human-centric: We first localize a human in the image and then determine the object relevant for the action and its spatial relation with the human. The model is learned automatically from a set of still images annotated only with the action label. Our approach relies on a human detector to initialize the model learning. For robustness to various degrees of visibility, we build a detector that learns to combine a set of existing part detectors. Starting from humans detected in a set of images depicting the action, our approach determines the action object and its spatial relation to the human. Its final output is a probabilistic model of the human-object interaction, i.e., the spatial relation between the human and the object. We present an extensive experimental evaluation on the sports action data set from [1], the PASCAL Action 2010 data set [2], and a new human-object interaction data set.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:34 ,  Issue: 3 )