On the basis of SVM( Support Vector Machine) multiclass classification，an improved DAGSVM ( Directed Acyclic Graph
Support Vector Machine) hand gesture recognition approach is put forward． Firstly，depth information of the scene is collected by using
Kinect sensor and hand region is obtained． Then feature vectors are extracted，which are used to train multiple binary SVM classifiers．
DAGSVM classifier is constructed using DAG topological structure with trained binary SVM classifiers and its structure sequence is improved．
Finally，the experimental results proved that the improved DAGSVM could reach higher recognition rate and can be used in the
control of intelligent wheelchair successfully．