Benchmarking RGB-D Segmentation: Toy Dataset of Complex Crowded Scenes

Aleksi Ikkala, Joni Pajarinen, Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Pages107-116
ISBN (Electronic)978-989-758-175-5
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventJoint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Rome, Italy
Duration: 27 Feb 201629 Feb 2016
Conference number: 11

Conference

ConferenceJoint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Abbreviated titleVISIGRAPP
CountryItaly
CityRome
Period27/02/201629/02/2016

Keywords

  • benchmarking
  • complex objects
  • dataset
  • object segmentation
  • real world objects
  • RGB-D segmentation

Fingerprint Dive into the research topics of 'Benchmarking RGB-D Segmentation: Toy Dataset of Complex Crowded Scenes'. Together they form a unique fingerprint.

Cite this