Gesture Estimation Method Based on Efficient MultiScale Fusion Network
Xiaoxuan Zhuang, Man Zhang, Jiahui Ming, Sihan Gao, Hongyao Chen, Jing Hai Zhu · 2023
Gesture estimation is a vital but challenge task for human-computer interaction. A desired gesture estimation method is supposed to be efficient for real-time requirement, yet still accurate. In this paper, we propose a novel network named Efficient Multi-Scale Fusion Net(EMF-Net), which effectively trades off between latency and accuracy. The cores of EMF-Net are two subnetworks, a efficient feature extraction network and a multi-scale fusion network. By presenting an improved feature extraction network to reduce latency of the model, and creating a multi-scale fusion scheme to alleviate the weak spatial generalization ability, the two subnetworks greatly improve accuracy while maintaining efficiency. Experimental results demonstrate our method attains superior accuracy with comparable computational burden.