Robust tracking of unknown objects through adaptive size estimation and appearance learning
2016 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2016, 559-566 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 559-566 p.
Adaptive learning mechanism, Appearance learning, Arbitrary shape, Object model, Rgb-d cameras, Robust tracking, Size estimation, Unknown objects, Robotics
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-197233DOI: 10.1109/ICRA.2016.7487179ScopusID: 2-s2.0-84977519696ISBN: 9781467380263OAI: oai:DiVA.org:kth-197233DiVA: diva2:1052727
2016 IEEE International Conference on Robotics and Automation, ICRA 2016, 16 May 2016 through 21 May 2016
QC 201612072016-12-072016-11-302016-12-07Bibliographically approved