Title :
Recognition of human actions by using depth information
Author :
Keceli, Ali Seydi ; Burak Can, Ahmet
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey
Abstract :
Usage of 3. dimension information obtained from depth sensors in human action recognition become important recently. Depth information can increase recognition accuracy in some applications. In this study, 10 different human actions are tried to recognize on a human model derived from Microsoft Kinect RGBD sensor. Angles between joints and displacement of joints on 3 koordinat axes are used as features. Actions are classified with the random forest and support vector machine approaches and 96% classification accuracy is obtained with the random forest approach.
Keywords :
decision trees; gesture recognition; image classification; image colour analysis; image sensors; support vector machines; 3 coordinate axes; 3 dimension information; Microsoft Kinect RGBD sensor; depth information; depth sensors; human action recognition; human actions; random approaches; random forest approach; support vector machine; Computer vision; Conferences; Hidden Markov models; Joints; Pattern recognition; Radio frequency; Support vector machines; Action recognition; Microsoft Kinect; random forest; support vector machine;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2013 21st
Conference_Location :
Haspolat
Print_ISBN :
978-1-4673-5562-9
Electronic_ISBN :
978-1-4673-5561-2
DOI :
10.1109/SIU.2013.6531211
