@misc{10481/86422,
year = {2016},
url = {https://hdl.handle.net/10481/86422},
abstract = {People detection and tracking is an interesting skill for interactive social
robots. Laser range finder (LRF) and vision based approaches are the most common
although both present strengths and weaknesses. In this paper, a multisensor system
to detect and track people in the proximity of a mobile robot is proposed. First, a
supervised learning approach is used to recognize patterns of legs in the proximity
of the robot using a LRF. After this, a tracking algorithm is developed using particle
filter and the observation model of legs. Second, a Kinect sensor is used to carry
out people detection and tracking. This second method uses a face detector in the
color image, the color of the clothes and the depth information. The strengths and
weaknesses of the second proposal are also commented. In order to put together the
strengths of both sensors, a third algorithm is proposed. In this third approach both
laser and Kinect data are fused to detect and track people. Finally, the multisensory
approach is experimentally evaluated in a real indoor environment. The multisensor
system outperforms the single sensor based approaches.},
organization = {This work have been partially supported by the Spanish Government project
TIN2012-38969.},
publisher = {Springer},
keywords = {People detection and tracking},
keywords = {Multisensor based tracking},
keywords = {Social robot},
keywords = {Human-robot interaction},
title = {A multisensor based approach using supervised learning and particle filtering for people detection and tracking},
doi = {https://doi.org/10.1007/978-3-319-27149-1_50},
author = {Aguirre Molina, Eugenio and García Silvente, Miguel and Pascual, Daniel},
}