View independent human posture identification using Kinect

As an important part of human computer interaction (HCI) system, posture identification has been extensively studied over last years. Recently, Microsoft Kinect Sensor has become a hot spot for posture identification because it is efficient in acquiring body joint location information. In this study, based on Kinect, we proposed a framework for view independent human posture identification. In this framework, a viewpoint rotation transformation was performed on original skeleton location data and then total 9 features were extracted for building a SVM classifier. About 4200 samples including five postures taken from different body orientations were collected to construct a dataset for performance evaluation. The results of PCA analysis showed that the transformation was efficient in distinguishing different postures. Further analysis demonstrated that this method achieved a superior performance of 98.0% when the orientation angle was between -60° and 60°. These results show that this view independent framework is powerful and efficient in viewpoint invariant posture identification.