Cascaded deep graphical convolutional neural network for 2D hand pose estimation

Abstract

Human hands, an essential component of the human body, play a vital role in interacting with and sensing real-world objects and are a reliable medium in modern technology for developing human-computer-interaction (HCI). Human Hand Pose Estimation (HPE) is challenging for numerous Artificial Intelligence (AI) applications due to the strong self-occlusion of the hands, depth ambiguity, and agile movement. Implementation of vision-based hand pose estimation algorithms can give a breath of innovation of these AI applications to overcome the challenges. We proposed a framework called Cascaded Deep Graphical Convolutional Neural Network (DCGCN, where Deep Convolutional Neural Network (DCnet) is used for computing unary and pairwise potential functions. A graphical model inference module is used for cascading unary and pairwise potentials. Evaluating the generated results via subjective and objective analysis, our DCDCN outperforms the state-of-the-art models in terms of accuracy and computational cost.