Hand Segmentation for Hand-Object Interaction from Depth map
This addresses robustness issues in applications like augmented reality and human-robot interaction by overcoming challenges with skin color and lighting variations.
The paper tackles hand segmentation for hand-object interaction by proposing a two-stage random decision forest method using only depth maps, achieving high accuracy and short processing time compared to state-of-the-art methods.
Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.