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Human Recognition in RGBD Combining Object Detectors and Conditional Random Fields

Amplianitis, Konstantinos and Hänsch, Ronny and Reulke, Ralf (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), pp. 655-663. Scitepress digital Library. VISIGRAPP 2016, Rom, Italien. DOI: 10.5220/0005786006550663

Full text not available from this repository.

Official URL: http://dx.doi.org/10.5220/0005786006550663

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 extend 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 pairwise measurements. Experimental results show that our proposed energy func- tion 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.

Item URL in elib:https://elib.dlr.de/116866/
Document Type:Conference or Workshop Item (Speech)
Title:Human Recognition in RGBD Combining Object Detectors and Conditional Random Fields
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Amplianitis, KonstantinosHU BerlinUNSPECIFIED
Hänsch, RonnyTechnical University of BerlinUNSPECIFIED
Reulke, RalfInstitut für Optische SensorsystemeUNSPECIFIED
Date:2016
Journal or Publication Title:Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 4)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.5220/0005786006550663
Page Range:pp. 655-663
Publisher:Scitepress digital Library
Status:Published
Keywords:Deformable Part Models, RGBD Data, Conditional Random Fields, Graph Cuts, Human Recognition
Event Title:VISIGRAPP 2016
Event Location:Rom, Italien
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Optische Technologien und Anwendungen
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Optical Sensor Systems
Deposited By: Dombrowski, Ute
Deposited On:19 Dec 2017 09:54
Last Modified:19 Dec 2017 09:54

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