Global Structured Feature Graph Convolutional Network for Skeleton-Based Action Recognition

Chia-Fen Hsieh, Po-Jen Liao
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Abstract

With the development of human action recognition technology, deep learning has been applied to still images, and great progress has been made. However, in film action recognition, there is still the issue of using deep learning to improve the recognition rates. When predicting the action of a movie, encountering occlusions, large background changes, or accumulation of some errors in consecutive frames in the movie, resulting in a decrease in the accuracy of action recognition and increase the difficulty of film action recognition. In addition, there is a lack of structural information of bone joints and related research between two different structures. To solve this problem, this paper proposed a joint structure related feature network method using graph convolution network (GCN), which combines multiple convolution kernels of different dimensions to enhance the recognition rate of movie actions. The experimental database was established in the laboratory of Nanyang Technological University, Singapore. The system uses the NTU RGB+D motion recognition data set to evaluate our network. Preliminary experimental results show that our system may improve accuracy and make it more efficient.
基于骨架的动作识别的全局结构特征图卷积网络
随着人体动作识别技术的发展,深度学习已经被应用到静止图像中,并取得了很大的进展。然而,在电影动作识别中,仍然存在使用深度学习来提高识别率的问题。在预测电影动作时,遇到遮挡,背景变化大,或者在电影连续帧中积累了一些误差,导致动作识别的准确性降低,增加了电影动作识别的难度。此外,骨关节的结构信息以及两种不同结构之间的相关研究也很缺乏。为了解决这一问题,本文提出了一种基于图卷积网络(GCN)的联合结构相关特征网络方法,该方法将多个不同维数的卷积核组合在一起,以提高电影动作的识别率。实验数据库建立在新加坡南洋理工大学实验室。系统使用NTU RGB+D运动识别数据集对我们的网络进行评估。初步实验结果表明,该系统可以提高系统的精度和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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