An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction
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AuthorshipPérez-Hurtado, Ignacio; Capitán, Jesús; Caballero, Fernando; Merino, Luis
Palabras claveHidden Markov models
Learning (artificial intelligence)
Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest are located at the end of human trajectories, but complete trajectories cannot always be observed by a mobile robot due to occlusions and people going out of sensor range. This paper extends GHMMs to deal with partial observed trajectories where people's goals are not known a priori. A novel technique based on hypothesis testing is also used to discover the points of interest (goals) in the environment. The approach is validated by predicting people's motion in three different datasets.
This work is partially funded by the EC-FP7 under grant agreement no. 611153 (TERESA) and the project PAIS-MultiRobot, funded by the Junta de Andalucía (TIC-7390). I. Perez-Hurtado is also supported by the Postdoctoral Junior Grant 2013 co-funded by the Spanish Ministry of Economy and Competitiveness and the Pablo de Olavide University.