Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation

Due to its importance in various applications that need human-computer interaction (HCI), the field of 3D hand pose estimation (HPE) has recently got a lot of attention. The use of technological developments, such as deep learning networks has accelerated the development of reliable 3D HPE systems. Therefore, in this paper, a 3D HPE based on Enhanced Global Pyramid Network (EGPNet) is proposed. Initially, feature extraction is done by backbone model of DetNetwork with improved EGPNet. The EGPNet is enhanced by the Smish activation function. After the feature extraction, the HPE is performed based on 3D pose correction network. Additionally, to enhance the estimation performance, Coot optimization algorithm is used to optimize the error between estimated and ground truth hand pose. The effectiveness of the proposed method is experimented on Bharatanatyam, yoga, Kathakali and sign language datasets with different networks in terms of area under the curve, median end-point-error (EPE) and mean EPE. The Coot optimization is also compared with existing optimization algorithms.