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Título

Behavior estimation for a complete framework for human motion prediction in crowded environments

AutorFerrer, Gonzalo ; Sanfeliu, Alberto
Fecha de publicación2014
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE International Conference on Robotics and Automation: 5940-5945 (2014)
ResumenIn the present work, we propose and validate a complete probabilistic framework for human motion prediction in urban or social environments. Additionally, we formulate a powerful and useful tool: the human motion behavior estimator. Three different basic behaviors have been detected: Aware, Balanced and Unaware. Our approach is based on the Social Force Model (SFM) and the intentionality prediction BHMIP. The main contribution of the present work is to make use of the behavior estimator for formulating a reliable prediction framework of human trajectories under the influence of dynamic crowds, robots, and in general any moving obstacle. Accordingly, we have demonstrated the great performance of our long-term prediction algorithm, in real scenarios, comparing to other prediction methods.
DescripciónPresentado al ICRA 2014 celebrado en Hong Kong del 31 de mayo al 7 de junio.
Versión del editorhttp://dx.doi.org/10.1109/ICRA.2014.6907734
URIhttp://hdl.handle.net/10261/127314
DOI10.1109/ICRA.2014.6907734
Identificadoresissn: 1050-4729
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