基于图卷积网络的行为识别方法综述
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青岛科技大学

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TP391

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国家自然科学基金项目(面上项目,重点项目,重大项目)


A Survey of Action Recognition Methods Based on Graph Convolution Network
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Qingdao University of Science and Technology

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    摘要:

    行为识别技术有巨大的应用前景和潜在的经济价值,广泛应用于视频监控、视频检索、人机交互、公共安全等领域。卷积神经网络虽被广泛应用,但对非欧式空间数据的处理具有局限性。而图卷积网络表现出基于图数据的依赖关系进行建模的强大功能,成为行为识别领域的研究热点。该文主要概述基于图卷积网络的行为识别方法。图卷积网络主要有两大方法,基于频谱的方法和基于空间的方法。该文从不同侧面对比分析了两种方法的优缺点,综述了两种方法分别在行为识别领域的应用与发展。最后,针对图卷积网络在行为识别中存在的问题,对未来图卷积网络的发展进行了展望。

    Abstract:

    Action recognition technology has great application prospects and potential economic value, and is widely used in video surveillance, video retrieval, human-computer interaction, public security and other fields. Although Convolutional Neural Network is widely used, it has limitations in dealing with data of non-Euclidean space. Graph Convolution Network shows the powerful function of modeling based on graph data dependency. It has become a research hotspot in the field of action recognition. This paper mainly summarizes the action recognition method based on Graph Convolution Network. There are two main methods of Graph Convolution Network: spectral-based method and spacial-based method. For the two methods, this paper analyzes the advantages and disadvantages from different aspects, and summarizes their application and development in the field of action recognition. Finally, according to the problems existing in the action recognition based on Graph Convolution Network, the future development of Graph Convolution Network is prospected.

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历史
  • 收稿日期:2020-05-04
  • 最后修改日期:2021-03-09
  • 录用日期:2020-11-03
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