(untitled) (Informatica, 2021)
Hand pose estimation plays an essential role in sign language understanding and human-computer interaction. Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of two main modules. The first one presents a segmentation-based approach to detect the hand skeleton and localize the hand bounding box. The second module regresses the 2D joint locations through a multi-scale heatmap regression approach that exploits the predicted hand skeleton as a constraint to guide the model. Moreover, we construct a new dataset that is suitable for both hand detection and pose estimation tasks. It includes the hand bounding boxes, the 2D keypoints, the 3D poses and their corresponding RGB images. We conduct extensive experiments on two datasets to validate our method. Qualitative and quantitative results demonstrate that the proposed method outperforms the state-of-the-art and recovers the pose even in cluttered images and complex poses.