PT Unknown
AU Miguel Reyes
   Gabriel Dominguez
   Sergio Escalera
TI Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data
BT 1st IEEE Workshop on Consumer Depth Cameras for Computer Vision
PY 2011
BP 1182
EP 1188
DI 10.1109/ICCVW.2011.6130384
AB We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach.
ER