Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
This work addresses the problem of accurate hand pose estimation for applications in computer vision and human-computer interaction, presenting an incremental improvement over existing methods.
The paper tackles hand pose estimation from a single depth image by proposing a Pose guided structured Region Ensemble Network (Pose-REN) that extracts and integrates features based on an initial pose estimate and hand joint topology, achieving state-of-the-art performance in experiments on public datasets.
Hand pose estimation from a single depth image is an essential topic in computer vision and human computer interaction. Despite recent advancements in this area promoted by convolutional neural network, accurate hand pose estimation is still a challenging problem. In this paper we propose a Pose guided structured Region Ensemble Network (Pose-REN) to boost the performance of hand pose estimation. The proposed method extracts regions from the feature maps of convolutional neural network under the guide of an initially estimated pose, generating more optimal and representative features for hand pose estimation. The extracted feature regions are then integrated hierarchically according to the topology of hand joints by employing tree-structured fully connections. A refined estimation of hand pose is directly regressed by the proposed network and the final hand pose is obtained by utilizing an iterative cascaded method. Comprehensive experiments on public hand pose datasets demonstrate that our proposed method outperforms state-of-the-art algorithms.