Abstract
GMM-SAMT, a new object tracking algorithm based on a combination of the mean shift principal and Gaussian mixture models (GMMs) is presented. GMM-SAMT stands for Gaussian mixture model based shape adaptive mean shift tracking. Instead of a symmetrical kernel like in traditional mean shift tracking, GMM-SAMT uses an asymmetric shape adapted kernel which is retrieved from an object mask. During the mean shift iterations the kernel scale is altered according to the object scale, providing an initial adaptation 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.
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Acknowledgments
This work has been supported by the Gesellschaft für Informatik, Automatisierung und Datenverarbeitung (iAd) and the Bundesministerium für Wirtschaft und Technologie (BMWi), ID 20V0801I.
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Quast, K., Kaup, A. (2013). Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models. In: Adami, N., Cavallaro, A., Leonardi, R., Migliorati, P. (eds) Analysis, Retrieval and Delivery of Multimedia Content. Lecture Notes in Electrical Engineering, vol 158. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3831-1_7
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DOI: https://doi.org/10.1007/978-1-4614-3831-1_7
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