LAUSR

Abstract Hand pose estimation plays an important role in human–computer interaction. The traditional way is to deal with a video stream frame by frame. However, since the gesture in the video is changing continuously, the adjacent frames must be highly related to each other. Therefore, the input of the neural network in this paper was set to be a series of contiguous video frames in order to make use of the relevance that the adjacent frames have. In this paper, a convolutional recurrent neural network (CRNN) module is proposed, which combines the characteristics of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), and can significantly improve the accuracy of the network.The impact of the location of the CRNN/RNN module in the network is also discussed in this paper. Finally, we demonstrated that our approach significantly outperforms the current state-of-the-art techniques in the NYU Hand dataset.