In order to perform tasks and offer socially acceptable human-robot interactions, domestic robots need the ability to collect various information about people. In this paper, we propose a framework that allows the extraction of high-level person features from a 2D camera in addition to tracking people over time. The proposed people management framework aggregates body and person features including an original pose estimation using only a 2D camera. At this time, people pose and posture, clothing colors, face recognition are combined with tracking and re-identification abilities. This framework has been successfully used by the LyonTech team in the RoboCup@Home 2018 competition with a Pepper robot from SoftBank Robotics where its utility for domestic robot applications was demonstrated.
CITATION STYLE
Saraydaryan, J., Leber, R., & Jumel, F. (2019). People Management Framework Using a 2D Camera for Human-Robot Social Interactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11531 LNAI, pp. 268–280). Springer. https://doi.org/10.1007/978-3-030-35699-6_21
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