Abstract—Human pose recognition has become an active research topic lately in the field of human computer interface (HCI). However it presents technical challenges due to the complexity of human motion. In this paper, we propose a novel methodology for human upper body pose recognition using labeled (i.e., recognized) human body parts in depth silhouettes. Our proposed method performs human upper body parts labeling using trained random forests (RFs) and utilizes support vector machines (SVMs) to recognize various upper body poses. To train RFs, we create a database of synthetic depth silhouettes of the upper body and their corresponding upper body parts labeled maps using a commercial computer graphics package. Once the body parts get labeled with the trained RFs, a skeletal upper body model is generated from the labeled body parts. Then, SVMs are trained with a set of joint angle features to recognize seven upper body poses. The experimental results show the mean recognition rate of 97.62%. Our proposed method should be useful as a near field HCI technique to be used in applications such as smart computer interfaces.
Index Terms—Upper body pose recognition, body parts labeling, random forests, support vector machines.
Myeong-Jun Lim, Jin-Ho Cho, Hee-Sok Han, and Tae-Seong Kim are with the Department of Biomedical Engineering, Kyung Hee University, Yong In, Republic of Korea (e-mail: firstname.lastname@example.org).
Cite: Myeong-Jun Lim, Jin-Ho Cho, Hee-Sok Han, and Tae-Seong Kim, "Upper Body Pose Recognition with Labeled Depth Body Parts via Random Forests and Support Vector Machines," International Journal of Information and Education Technology vol. 3, no. 1, pp. 67-71, 2013.