ITP: Inverse Trajectory Planning for Human Pose Prediction

dc.contributor.author	Peña, Pedro A.
dc.contributor.author	Visser, Ubbo
dc.date.accessioned	2021-04-23T09:34:07Z
dc.date.available	2021-04-23T09:34:07Z
dc.date.issued	2020
dc.description.abstract	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.	de
dc.identifier.doi	10.1007/s13218-020-00658-7
dc.identifier.pissn	1610-1987
dc.identifier.uri	http://dx.doi.org/10.1007/s13218-020-00658-7
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/36289
dc.publisher	Springer
dc.relation.ispartof	KI - Künstliche Intelligenz: Vol. 34, No. 2
dc.relation.ispartofseries	KI - Künstliche Intelligenz
dc.title	ITP: Inverse Trajectory Planning for Human Pose Prediction	de
dc.type	Text/Journal Article
gi.citation.endPage	225
gi.citation.startPage	209

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