loading
Documents

Research.Publish.Connect.

Paper

Authors: Konstantinos Amplianitis 1 ; Ronny Hänsch 2 and Ralf Reulke 1

Affiliations: 1 Humboldt-Universität zu Berlin, Germany ; 2 Technische Universität Berlin, Germany

ISBN: 978-989-758-175-5

Keyword(s): Deformable Part Models, RGBD Data, Conditional Random Fields, Graph Cuts, Human Recognition.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: This paper addresses the problem of detecting and segmenting human instances in a point cloud. Both fields have been well studied during the last decades showing impressive results, not only in accuracy but also in computational performance. With the rapid use of depth sensors, a resurgent need for improving existing state-of-the-art algorithms, integrating depth information as an additional constraint became more ostensible. Current challenges involve combining RGB and depth information for reasoning about location and spatial extent of the object of interest. We make use of an improved deformable part model algorithm, allowing to deform the individual parts across multiple scales, approximating the location of the person in the scene and a conditional random field energy function for specifying the object’s spatial extent. Our proposed energy function models up to pairwise relations defined in the RGBD domain, enforcing label consistency for regions sharing similar unary and pairwis e measurements. Experimental results show that our proposed energy function provides a fairly precise segmentation even when the resulting detection box is imprecise. Reasoning about the detection algorithm could potentially enhance the quality of the detection box allowing capturing the object of interest as a whole. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.82.79.137

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Amplianitis K., Hänsch R. and Reulke R. (2016). Human Recognition in RGBD Combining Object Detectors and Conditional Random Fields.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 655-663. DOI: 10.5220/0005786006550663

@conference{visapp16,
author={Konstantinos Amplianitis and Ronny Hänsch and Ralf Reulke},
title={Human Recognition in RGBD Combining Object Detectors and Conditional Random Fields},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={655-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005786006550663},
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, (VISIGRAPP 2016)
TI - Human Recognition in RGBD Combining Object Detectors and Conditional Random Fields
SN - 978-989-758-175-5
AU - Amplianitis K.
AU - Hänsch R.
AU - Reulke R.
PY - 2016
SP - 655
EP - 663
DO - 10.5220/0005786006550663

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.