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Authors: Aleksi Ikkala ; Joni Pajarinen and Ville Kyrki

Affiliation: Aalto University, Finland

ISBN: 978-989-758-175-5

Keyword(s): Object Segmentation, RGB-D Segmentation, Benchmarking, Dataset, Complex Objects, Real World Objects.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Pattern Recognition ; Robotics ; Segmentation and Grouping ; Software Engineering

Abstract: In this paper we present a new RGB-D dataset captured with the Kinect sensor. The dataset is composed of typical children’s toys and contains a total of 449 RGB-D images alongside with their annotated ground truth images. Compared to existing RBG-D object segmentation datasets, the objects in our proposed dataset have more complex shapes and less texture. The images are also crowded and thus highly occluded. Three state-of-the-art segmentation methods are benchmarked using the dataset. These methods attack the problem of object segmentation from different starting points, providing a comprehensive view on the properties of the proposed dataset as well as the state-of-the-art performance. The results are mostly satisfactory but there remains plenty of room for improvement. This novel dataset thus poses the next challenge in the area of RGB-D object segmentation.

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Paper citation in several formats:
Ikkala, A.; Pajarinen, J. and Kyrki, V. (2016). Benchmarking RGB-D Segmentation: Toy Dataset of Complex Crowded Scenes.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 107-116. DOI: 10.5220/0005675501070116

@conference{visapp16,
author={Aleksi Ikkala. and Joni Pajarinen. and Ville Kyrki.},
title={Benchmarking RGB-D Segmentation: Toy Dataset of Complex Crowded Scenes},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={107-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005675501070116},
isbn={978-989-758-175-5},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, (VISIGRAPP 2016)
TI - Benchmarking RGB-D Segmentation: Toy Dataset of Complex Crowded Scenes
SN - 978-989-758-175-5
AU - Ikkala, A.
AU - Pajarinen, J.
AU - Kyrki, V.
PY - 2016
SP - 107
EP - 116
DO - 10.5220/0005675501070116

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