Robust tracking of unknown objects through adaptive size estimation and appearance learning
Alessandro Pieropan, Niklas Bergström, Masatoshi Ishikawa, Danica Kragic, Hedvig Kjellström
Abstract
This work employs an adaptive learning mechanism to perform tracking of an unknown object through RGBD cameras. We extend our previous framework to robustly track a wider range of arbitrarily shaped objects by adapting the model to the measured object size. The size is estimated as the object undergoes motion, which is done by fitting an inscribed cuboid to the measurements. The region spanned by this cuboid is used during tracking, to determine whether or not new measurements should be added to the object model. In our experiments we test our tracker with a set of objects of arbitrary shape and we show the benefit of the proposed model due to its ability to adapt to the object shape which leads to more robust tracking results.
BibTeX
@inproceedings{icra2016_robusttrackingof,
title = {Robust tracking of unknown objects through adaptive size estimation and appearance learning},
author = {Alessandro Pieropan and Niklas Bergström and Masatoshi Ishikawa and Danica Kragic and Hedvig Kjellström},
booktitle = {ICRA 2016},
year = {2016}
}