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. © 2013 Springer Science+Business Media.
CITATION STYLE
Quast, K., & Kaup, A. (2013). Shape adaptive mean shift object tracking using gaussian mixture models. In Lecture Notes in Electrical Engineering (Vol. 158 LNEE, pp. 107–122). https://doi.org/10.1007/978-1-4614-3831-1_7
Mendeley helps you to discover research relevant for your work.