Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models

Quast K, Kaup A (2010)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2010

Event location: Desenzano del Garda IT

ISBN: 978-88-905328-0-1

URI: https://ieeexplore.ieee.org/document/5617670

Abstract

GMM-SAMT, a new object tracking algorithm based on a combination of the mean shift principal and Gaussian mixture models is presented. GMM-SAMT uses an asymmetric shape adapted kernel, instead of a symmetrical one like in traditional mean shift tracking. During the mean shift iterations the kernel scale is altered according to the object scale, providing an initial adaption of the object shape. The final shape of the kernel is then obtained by segmenting the area inside and around the adapted kernel into object and non-object segments using Gaussian mixture models.

Authors with CRIS profile

Katharina Quast Lehrstuhl für Multimediakommunikation und Signalverarbeitung Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung

How to cite

APA:

Quast, K., & Kaup, A. (2010). Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models. In Proceedings of the 11th IEEE International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS). Desenzano del Garda, IT.

MLA:

Quast, Katharina, and André Kaup. "Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models." Proceedings of the 11th IEEE International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Desenzano del Garda 2010.

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