Authors:
Arnaud Boucher
1
;
Olivier Martinot
2
and
Nicole Vincent
3
Affiliations:
1
University of Burgundy, France
;
2
Alcatel Lucent, France
;
3
Paris Descartes University, France
Keyword(s):
Video Segmentation, Depth+RGB Data, Confidence Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
The paper addresses the problem of people extraction in a closed context in a video sequence including colour and depth information. The study is based on low cost depth captor included in products such as Kinect or Asus devices that contain a couple of cameras, colour and depth cameras. Depth cameras lack precision especially where a discontinuity in depth occur and some times fail to give an answer. Colour information may be ambiguous to discriminate between background and foreground. This made us use first depth information to achieve a coarse segmentation that is improved with colour information. Furthermore, color information is only used when a classification in two classes of fore/background pixels is clear enough. The developed method provides a reliable and robust segmentation and a natural visual rendering, while maintaining a real time processing.