Abstract:
KINECT has been recently introduced in the market as a low cost 3D acquisition
device, so it will be interesting to discover the power of this device when we use it
for gesture recognition. In this thesis, we propose a real-time gesture recognition
system using 3D sensor that transforms gestures into a set of useful words using
different machine learning algorithms and taking into consideration temporal features. A depth image, which is provided by KINECT, will be used to construct a
skeleton of the human body. We have used Nearest Neighbor (NN) with different
distance formulas, Self Organizing Map (SOM) and Hidden Markov Model (HMM)
for recognition. The result of this thesis using the 10 fold cross validation shows that
HMM may provide recognition accuracy up to 96 percent, while using NN algorithm
with Spearman distance we can obtain around 90 percent accuracy and around 75
percent of accuracy using the SOM algorithm. All three algorithms has works in
real-time
Description:
no of pages 115, 27260, informatics 1/2012 , in the store
