Real-time moving object detection in video sequences using spatio-temporal adaptive Gaussian mixture models

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Quast K, Obermann M, Kaup A
Publication year: 2010
Volume: 1
Pages range: 413-418
ISBN: 9789896740283
Language: English


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.


FAU Authors / FAU Editors

Kaup, André Prof. Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Quast, Katharina Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


How to cite

APA:
Quast, K., Obermann, M., & Kaup, A. (2010). Real-time moving object detection in video sequences using spatio-temporal adaptive Gaussian mixture models. In Proceedings of the 5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 (pp. 413-418). Angers, FR.

MLA:
Quast, Katharina, Matthias Obermann, and André Kaup. "Real-time moving object detection in video sequences using spatio-temporal adaptive Gaussian mixture models." Proceedings of the 5th International Conference on Computer Vision Theory and Applications, VISAPP 2010, Angers 2010. 413-418.

BibTeX: 

Last updated on 2019-08-05 at 15:53