%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2006/07.22.18.53
%2 sid.inpe.br/sibgrapi@80/2006/07.22.18.53.31
%@doi 10.1109/SIBGRAPI.2006.46
%T Unscented KLT: nonlinear feature and uncertainty tracking
%D 2006
%A Dorini, Leyza Elmeri Baldo,
%A Goldenstein, Siome Klein,
%@affiliation Unicamp - Universidade Estadual de Campinas
%@affiliation Unicamp - Universidade Estadual de Campinas
%E Oliveira Neto, Manuel Menezes de,
%E Carceroni, Rodrigo Lima,
%B Brazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
%C Manaus, AM, Brazil
%8 8-11 Oct. 2006
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K feature tracking, uncertainty tracking, outlier rejection.
%X Accurate feature tracking is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do not maintain information about the error of the data being tracked. In this paper, we propose a new generic framework that uses the Scaled Unscented Transform (SUT) to augment arbitrary feature tracking algorithms, by introducing Gaussian Random Variables (GRV) for the representation of features' locations uncertainties. Here, we apply the framework to the well-understood Kanade-Lucas-Tomasi (KLT) feature tracker, giving birth to what we call Unscented KLT (UKLT). It tracks probabilistic confidences and better rejects errors, all on-line, and leads to more robust computer vision applications. We also validade the experiments with a bundle adjustment procedure, using real and synthetic sequences.
%@language en
%3 dorini-Uklt.pdf