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Authors: Katharina Quast ; Matthias Obermann and André Kaup

Affiliation: University of Erlangen-Nuremberg, Germany

Keyword(s): Object detection, Background modeling.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Human-Computer Interaction ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Physiological Computing Systems ; Real-Time Vision

Abstract: In this paper we present a background subtraction method for moving object detection based on Gaussian mixture models which performs in real-time. Our method improves the traditional Gaussian mixture model (GMM) technique in several ways. It takes into account spatial and temporal dependencies, as well as a limitation of the standard deviation leading to a faster update of the model and a smoother object mask. A shadow detection method which is able to remove the umbra as well as the penumbra in one single processing step is further used to get a mask that fits the object outline even better. Using the computational power of parallel computing we further speed up the object detection process.

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Paper citation in several formats:
Quast, K.; Obermann, M. and Kaup, A. (2010). REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS. In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3; ISSN 2184-4321, pages 413-418. DOI: 10.5220/0002816904130418

@conference{visapp10,
author={Katharina Quast. and Matthias Obermann. and André Kaup.},
title={REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={413-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002816904130418},
isbn={978-989-674-028-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - REAL-TIME MOVING OBJECT DETECTION IN VIDEO SEQUENCES USING SPATIO-TEMPORAL ADAPTIVE GAUSSIAN MIXTURE MODELS
SN - 978-989-674-028-3
IS - 2184-4321
AU - Quast, K.
AU - Obermann, M.
AU - Kaup, A.
PY - 2010
SP - 413
EP - 418
DO - 10.5220/0002816904130418