Moving Object Detection by Connected Component Labeling of Point Cloud Registration Outliers on the GPU

Michael Korn, Daniel Sanders, Josef Pauli

Abstract

Using a depth camera, the KinectFusion with Moving Objects Tracking (KinFu MOT) algorithm permits tracking the camera poses and building a dense 3D reconstruction of the environment which can also contain moving objects. The GPU processing pipeline allows this simultaneously and in real-time. During the reconstruction, yet untraced moving objects are detected and new models are initialized. The original approach to detect unknown moving objects is not very precise and may include wrong vertices. This paper describes an improvement of the detection based on connected component labeling (CCL) on the GPU. To achieve this, three CCL algorithms are compared. Afterwards, the migration into KinFu MOT is described. It incorporates the 3D structure of the scene and three plausibility criteria refine the detection. In addition, potential benefits on the CCL runtime of CUDA Dynamic Parallelism and of skipping termination condition checks are investigated. Finally, the enhancement of the detection performance and the reduction of response time and computational effort is shown.

References

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Paper Citation


in Harvard Style

Korn M., Sanders D. and Pauli J. (2017). Moving Object Detection by Connected Component Labeling of Point Cloud Registration Outliers on the GPU . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 499-508. DOI: 10.5220/0006173704990508


in Bibtex Style

@conference{visapp17,
author={Michael Korn and Daniel Sanders and Josef Pauli},
title={Moving Object Detection by Connected Component Labeling of Point Cloud Registration Outliers on the GPU},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={499-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006173704990508},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Moving Object Detection by Connected Component Labeling of Point Cloud Registration Outliers on the GPU
SN - 978-989-758-227-1
AU - Korn M.
AU - Sanders D.
AU - Pauli J.
PY - 2017
SP - 499
EP - 508
DO - 10.5220/0006173704990508