TY  - JOUR
AU  - Meysam Madadi
AU  - Sergio Escalera
AU  - Alex Carruesco Llorens
AU  - Carlos Andujar
AU  - Xavier Baro
AU  - Jordi Gonzalez
PY  - 2018//
TI  - Top-down model fitting for hand pose recovery in sequences of depth images
T2  - IMAVIS
JO  - Image and Vision Computing
SP  - 63
EP  - 75
VL  - 79
N2  - 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.
UR  - https://doi.org/10.1016/j.imavis.2018.09.006
L1  - http://refbase.cvc.uab.es/files/MEC2018.pdf
UR  - http://dx.doi.org/10.1016/j.imavis.2018.09.006
N1  - HUPBA; 600.098
ID  - Meysam Madadi2018
ER  -