Human arm estimation using convex features in depth images

Human arm estimation is very important for HCI and human pose estimation, but it remains a challenging problem due to complex environments, various poses, etc. In this paper, an efficient and robust method is presented to estimate human arms from depth images. Firstly, three convex features are explored from depth images for lower/upper arm detection, including convex degree feature (CDF), convex region feature (CRF), and U-type depth feature (UDF). With these effective features, secondly, the upper arm and lower arm candidates are accurately and quickly detected in a parallel and complementary way. Finally, the full arm is estimated based on depth continuity and human configuration constraint. Experiments on lots of test images demonstrate the robustness and efficacy of this approach.