%0 Journal Article
%T Top-down model fitting for hand pose recovery in sequences of depth images
%A Meysam Madadi
%A Sergio Escalera
%A Alex Carruesco Llorens
%A Carlos Andujar
%A Xavier Baro
%A Jordi Gonzalez
%J Image and Vision Computing
%D 2018
%V 79
%F Meysam Madadi2018
%O HUPBA; 600.098
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3203), last updated on Tue, 25 Jan 2022 09:21:08 +0100
%X State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.
%U https://doi.org/10.1016/j.imavis.2018.09.006
%U http://refbase.cvc.uab.es/files/MEC2018.pdf
%U http://dx.doi.org/10.1016/j.imavis.2018.09.006
%P 63-75