Hand Segmentation for Hand-Object Interaction from Depth map

Hand-object interaction is important for many applications such as augmented reality, medical application, and human-robot interaction. To understand hand-object interaction, hand segmentation is a necessary pre-process. However, current method is based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Therefore, we propose the first hand segmentation method for hand-object interaction using only depth map. The proposed method includes randomized decision forest (RDF), bilateral filtering, decision adjustment, and post-processing. We demonstrate the effectiveness of the method by testing for five objects. The method achieves the average $F_1$ score of 0.8409 and 0.8163 for the same object and new object, respectively. Also, the method takes less than 10$ms$ to process each frame.