Regression-Based 3D Hand Pose Estimation for Human-Robot Interaction

In shared workspaces where humans and robots interact, a significant task is to hand over objects. The process of hand over needs to be reliable, a human must not be injured during the process, hence reliable tracking of human hands is necessary. To avoid collision, we apply an encoder-decoder based 2D and 3D key-point regression network on color images. In this paper, we introduce a complete pipeline with the idea of stacked and cascaded convolutional neural networks and tune the parameters of the network for real-time applications. Experiments are conducted on multiple datasets, with low and high occlusions and we evaluate the trained models on multiple datasets for the human-robot interaction test set.