Authors:
Konstantinos Amplianitis
1
;
Ronny Hänsch
2
and
Ralf Reulke
1
Affiliations:
1
Humboldt-Universität zu Berlin, Germany
;
2
Technische Universität Berlin, Germany
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 pairwi
se 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)