Dynamic Gesture Recognition Based on iCPM and RNN. (November 2020)

• Record Type: Journal Article
• Authors: Weihui, Chen; Yichao, Fan; Ye, Zhang
• Is Part Of: Journal of physics. Volume 1684(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1684/1/012066
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Aim to solve the problem that continuous complex actions are difficult to be recognized by computer vision technology in actual production, this paper collects gesture video from single-and double-viewpoint, constructs gesture joint point coordinate recognition network model by improved Convolutional Pose Machine(iCPM) model, obtains gesture Gauss heat map and joint point coordinates in each frame of video, and then inputs joint point coordinates with time series into gesture sequence recognition network. Finally, the gesture sequence is obtained. The experimental results show that the recognition accuracy of gesture recognition model based on double-viewpoint is 4.18% ∼ 6.92% higher than that of the single-viewpoint gesture recognition model, which verifies that the gesture recognition model based on dual-viewpoint has better recognition effect. In addition, the video samples of Tin-Lead Soldering including continuous and complex actions verify that the model has ideal recognition ability for dynamic gesture in actual production process.