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DescriptionDomains where human behavior understanding is a crucial need (e.g., robotics, human-computer interaction, affective computing, and social signal processing) rely on advanced pattern recognition techniques to automatically interpret complex behavioral patterns generated when humans interact with machines or with others. This is a challenging problem where many issues are still open, including the joint modeling of behavioral cues taking place at different time scales, the inherent uncertainty of machine detectable evidences of human behavior, the mutual influence of people involved in interactions, the presence of long term dependencies in observations extracted from human behavior, and the important role of dynamics in human behavior understanding. Implementing these methods on robotic platforms introduces further constraints on processing resources, tracking over time, model building, and generalization. This workshop, organized as a satellite to IROS'2012, will gather researchers dealing with the problem of modeling human behavior under its multiple facets (expression of emotions, display of relational attitudes, performance of individual or joint actions, imitation, etc.), with particular attention to implications in robotics, including additional resource and robustness constraints of robotic platforms, social aspects of human-robot interaction, and developmental approaches to robotics.
ContactDr. Albert Ali Salah |
