Incorporating global and local observation models for human pose tracking

Tracking human pose is attractive to many applications such as Human Robot Interface (HRI), motion capture system, video surveillance, action recognition, etc. Though various methods were introduced during last decades, including both color and depth camera based, it is still considered that feature sets for them are not discriminative enough. In this paper, we propose a human pose tracking method based on a graphical model which incorporates global and local feature sets including Histogram of Oriented Gradients (HOG) and color distribution. HumanEva-I dataset is used for testing effectiveness of the proposed method.