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ITP: Inverse Trajectory Planning for Human Pose Prediction

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2020

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Springer

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Tracking and predicting humans in three dimensional space in order to know the location and heading of the human in the environment is a difficult task. Though if solved it will allow a robotic agent to know where it can safely be and navigate the environment without imposing any danger to the human that it is interacting with. We propose a novel probabilistic framework for robotic systems in which multiple models can be fused into a circular probabilitymap to forecast human poses. We developed and implemented the framework and tested it on Toyota’s HSR robot and Waymo Open Dataset. Our experiments show promising results.

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Peña, Pedro A.; Visser, Ubbo (2020): ITP: Inverse Trajectory Planning for Human Pose Prediction. KI - Künstliche Intelligenz: Vol. 34, No. 2. DOI: 10.1007/s13218-020-00658-7. Springer. PISSN: 1610-1987. pp. 209-225

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