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.

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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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