Shape adaptive mean shift object tracking using Gaussian mixture models

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.