Kernel-based Tracking from a Probabilistic Viewpoint
In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixels in both, the target model and its candidate as random variables and make use of a generative model so as to cast the tracking task into a maximum likelihood framework. This, in turn, permits the use of the EM-algorithm to estimate a set of latent variables that can be used to update the target-center position. Once...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR 2007)|
|01_Nguyen_Kernel-based_Tracking_from_a_2007.pdf||993.54 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.