Local Regression Based Hourglass Network for Hand Pose Estimation from a Single Depth Image
Jia Li, Zengfu Wang · 2018
Hand pose estimation plays an important role in many applications such as human-computer interaction. With the advent of commodity depth sensors and the developments of deep learning, noticeable improvements have been made in this field recently. Nevertheless, the accuracy and robustness of existing approaches are still dissatisfying. In this paper, we propose an end-to-end local regression based hourglass network with a modified loss function to estimate the 3D pose of the hand in a depth image. We use a third order hourglass block to extract features of the hand. At the top of our network, we slice the feature map into several regions and regress the regions independently first. Then, we merge the regression results and feed them to the final regressor. Besides, we compare performances of different loss functions for the task. The results indicate that the structure of the network and the loss function designed here lead to an obvious improvement. And the proposed approach is comparable to, or superior to the state-of-the-art on a public challenging dataset. Our system can run at over 910 FPS on a single GPU, and the mean error of estimation is reduced to 12.36 mm.