2D Hand Pose Estimation

What is it about?

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). 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 HPE algorithms can give a breath of innovation to these AI applications to overcome the challenges. We investigated a framework called Cascaded Deep Graphical Convolutional Neural Network (CDGCN), where Deep Convolutional Neural Network (DCNN) 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 CDGCN outperforms the state-of-the-art models in terms of accuracy and computational cost.

Featured Image

Photo by Luis Quintero on Unsplash

Photo by Luis Quintero on Unsplash

Why is it important?

Simple Baseline model for new Researchers.