TY  - JOUR
AU  - Reza Azad
AU  - Maryam Asadi-Aghbolaghi
AU  - Shohreh Kasaei
AU  - Sergio Escalera
PY  - 2019//
TI  - Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps
T2  - TCSVT
JO  - IEEE Transactions on Circuits and Systems for Video Technology
SP  - 1729
EP  - 1740
VL  - 29
IS  - 6
KW  - Hand gesture recognition
KW  - Multilevel temporal sampling
KW  - Weighted depth motion map
KW  - Spatio-temporal description
KW  - VLAD encoding
N2  - Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset.
UR  - http://dx.doi.org/10.1109/TCSVT.2018.2855416
N1  - HUPBA; no proj
ID  - Reza Azad2019
ER  -