By Albert Ali Salah, Javier Ruiz-del-Solar, Çetin Meriçli, Pierre-Yves Oudeyer (auth.), Albert Ali Salah, Javier Ruiz-del-Solar, Çetin Meriçli, Pierre-Yves Oudeyer (eds.)
This publication constitutes the refereed complaints of the 3rd Workshop on Human habit realizing, HBU 2012, held in Vilamoura, Portugal, in October 2012. The 14 revised papers offered have been rigorously reviewed and chosen from 31 submissions. The papers are equipped in topical sections on sensing human habit; social and affective indications; human-robot interplay; imitation and studying from demonstration.
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Additional resources for Human Behavior Understanding: Third International Workshop, HBU 2012, Vilamoura, Portugal, October 7, 2012. Proceedings
Here we restrict ourselves to a laser scanner on the robot and an overhead camera fixed in the room. The contribution of our work is that by mapping all information into a probabilistic model, the system can be easily extended with other sensors such as multiple cameras or RGB-D cameras, and is robust to the absence of sensors. In the next sections we will describe related work and systems. In section 4 our own model will be introduced. The sections after that will describe the likelihood functions for the laser on the robot and the camera in the room.
In principle, the energy should be lower for assignments that correspond to a realistic transformation from the model image to the scene image, and it should be high otherwise. We search for the assignments that minimize this energy. Using pairwise edges mostly restricts geometrical coherence constraints to distance similarities, which are not invariant to scale changes. Higher order matching through hyper-graphs has been proposed in the context of object recognition . Typically, 1 A hyper-graph is a generalization of a graph, where a hyper-edge can connect any number of vertices, typically more than two .
Another common approach is to inherently learn features proceeding from multiple views by applying Feature Fusion. The combination of multiple features in order to be considered as a single complex feature can be achieved, for instance, by concatenating  or averaging  the single-view features. Similarly to the information fusion applied at decision-level, combination of this technique with single-view action recognition methods is relatively eﬀortless. Nonetheless, the complexity of classiﬁcation may increase when adding further views.
Human Behavior Understanding: Third International Workshop, HBU 2012, Vilamoura, Portugal, October 7, 2012. Proceedings by Albert Ali Salah, Javier Ruiz-del-Solar, Çetin Meriçli, Pierre-Yves Oudeyer (auth.), Albert Ali Salah, Javier Ruiz-del-Solar, Çetin Meriçli, Pierre-Yves Oudeyer (eds.)