A Novel Framework of Hand Localization and Hand Pose Estimation

In this paper, we propose a novel framework for hand localization and pose estimation from a single depth image. For hand localization, unlike most existing methods that using heuristic strategies, e.g. color segmentation, we propose Hierarchical Hand location Networks (HHLN) to estimate the hand location from coarse to fine in depth images, which is robust to the complex environment and efficient. It first applied at a low-resolution octree of the whole depth image and produced coarse hand region and then constructs the hand region into a high-resolution octree for fine location estimation. For pose estimation, we propose Wide Receptive-filed (WR-OCNN) which is able to capture meaningful hand structure in different scales and estimate the 3D hand pose accurately. Experiments on two widely-used hand datasets(NYU dataset and ICVL dataset) demonstrate the effectiveness and superiority of the proposed framework.