PT Journal
AU Meysam Madadi
   Sergio Escalera
   Alex Carruesco Llorens
   Carlos Andujar
   Xavier Baro
   Jordi Gonzalez
TI Top-down model fitting for hand pose recovery in sequences of depth images
SO Image and Vision Computing
JI IMAVIS
PY 2018
BP 63
EP 75
VL 79
DI 10.1016/j.imavis.2018.09.006
AB 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.
ER