Robust human tracking using statistical human shape model with postural variation

Human tracking in monocular image sequences has been studied in the field of computer vision for many kinds of applications such as surveillance system, intelligent room, sports video analysis and so on. Human tracking in real environment is challenging topic due to various factors such as illumination change, partial or almost complete occlusion of human body, and wide variety of body shapes. In this paper, we present a robust human tracking using statistical human shape model of appearance variation with postural change. Our part-based statistical human model can generate learned appearances of main human poses, and enables effective and robust human tracking with simple features such silhouette, edge and color. Our proposed method achieves human tracking robust not only to partial occlusion but also to postural change. The experimental results validate the robustness of our methods in the real indoor environments.