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

Quast K, Obermann M, Kaup A (2010)

Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2010

Book Volume: 1

Pages Range: 413-418

Event location: Angers FR

ISBN: 9789896740283

URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77956329028∨igin=inward

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.

Authors with CRIS profile

Katharina Quast Lehrstuhl für Multimediakommunikation und Signalverarbeitung Andre Kaup 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.

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