Recognition of human actions by using depth information

Author

Keceli, Ali Seydi ; Burak Can, Ahmet

Author_Institution

Bilgisayar Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/SIU.2013.6531211

Filename

6531211