Detail publikace

Robust motion segmentation for on-line application

Originální název

Robust motion segmentation for on-line application

Anglický název

Robust motion segmentation for on-line application

Jazyk

en

Originální abstrakt

This paper presents a novel approach for on-line video motion segmentation. Common methods were designed for off-line processing, where time to process one frame is not so important and varies from minutes to hours. The motivation of our work was an application in robotic perception, where a high computational speed is required. The main contribution of this work is an adaptation of existing methods to a higher computational speed and on-line processing. The proposed approach is based on sparse features, we utilized the KLT tracker to obtain their trajectories. A RANSAC-based method is used for initial motion segmentation, resulting motion groups are partitioned by a spatial-proximity constraints. The correspondence of motion groups across frames is solved by one-frame label propagation in forward and backward directions. Finally, an approximation of dense image segmentation is obtained by using the Voronoi tessellation.

Anglický abstrakt

This paper presents a novel approach for on-line video motion segmentation. Common methods were designed for off-line processing, where time to process one frame is not so important and varies from minutes to hours. The motivation of our work was an application in robotic perception, where a high computational speed is required. The main contribution of this work is an adaptation of existing methods to a higher computational speed and on-line processing. The proposed approach is based on sparse features, we utilized the KLT tracker to obtain their trajectories. A RANSAC-based method is used for initial motion segmentation, resulting motion groups are partitioned by a spatial-proximity constraints. The correspondence of motion groups across frames is solved by one-frame label propagation in forward and backward directions. Finally, an approximation of dense image segmentation is obtained by using the Voronoi tessellation.

BibTex


@inproceedings{BUT91497,
  author="Lukáš {Klicnar} and Vítězslav {Beran}",
  title="Robust motion segmentation for on-line application",
  annote="This paper presents a novel approach for on-line video motion segmentation.
Common methods were designed for off-line processing, where time to process one
frame is not so important and varies from minutes to hours. The motivation of our
work was an application in robotic perception, where a high computational speed
is required. The main contribution of this work is an adaptation of existing
methods to a higher computational speed and on-line processing. The proposed
approach is based on sparse features, we utilized the KLT tracker to obtain their
trajectories. A RANSAC-based method is used for initial motion segmentation,
resulting motion groups are partitioned by a spatial-proximity constraints. The
correspondence of motion groups across frames is solved by one-frame label
propagation in forward and backward directions. Finally, an approximation of
dense image segmentation is obtained by using the Voronoi tessellation.",
  address="University of West Bohemia in Pilsen",
  booktitle="Proceedings of WSCG'12",
  chapter="91497",
  edition="20th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2012 - Conference Proceedings",
  howpublished="online",
  institution="University of West Bohemia in Pilsen",
  year="2012",
  month="december",
  pages="205--212",
  publisher="University of West Bohemia in Pilsen",
  type="conference paper"
}