1. 연구내용
(1) 연구요약
This study introduces a novel method to recognize the human gesture using binary decision tree and Multi-class Support Vector Machine (MCSVM). In a learning stage, 3D trajectory of the human gesture by a kinect sensor is assigned into the tree node of the binary decision tree according to its distribution property. The user’s gesture trajectory is re-sampled and normalized, and we extract the chain code histogram at a regular interval. After training MCSVM in each node, we are able to recognize the human gestures.
(2) 연구결과
J. Oh, T. Kim, and H. Hong, "Using binary decision tree and multiclass SVM for human gesture recognition," Proc. of Int. Conf. on Information Science and Applications, June 2013.