TY  - CONF
AU  - Meysam Madadi
AU  - Sergio Escalera
AU  - Alex Carruesco
AU  - Carlos Andujar
AU  - Xavier Baro
AU  - Jordi Gonzalez
A2  - FG
PY  - 2017//
TI  - Occlusion Aware Hand Pose Recovery from Sequences of Depth Images
BT  - 12th IEEE International Conference on Automatic Face and Gesture Recognition
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. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.
L1  - http://refbase.cvc.uab.es/files/MEC2017.pdf
UR  - http://dx.doi.org/10.1109/FG.2017.37
N1  - HUPBA; ISE; 602.143; 600.098; 600.119
ID  - Meysam Madadi2017
ER  -