摘要： 过去几年，视频行为分类从手工选择特征方式逐步向采用深度学习端到端网络模型方式转变。讨论了传统手工选择特征的行为分类方法以及基于深度学习的行为分类方法，着重对包括基于卷积神经网络、长短期记忆网络和时空融合网络等不同的深度学习方法进行了论述，并对常用视频行为分类数据集做了概述，对视频行为分类方法的发展进行总结和展望。

中图分类号： TP391
文献标识码： A
DOI：10.16157/j.issn.0258-7998.212388
中文引用格式： 杨戈，邹武星. 基于深度学习的视频行为分类方法综述[J].电子技术应用，2022，48(7)：1-7，12.
英文引用格式： Yang Ge，Zou Wuxing. A survey on video action classification methods based on deep learning[J]. Application of Electronic Technique，2022，48(7)：1-7，12.

A survey on video action classification methods based on deep learning

Abstract： In the past few years, video action classification has gradually changed from manual feature selection to deep learning end-to-end model. This article discusses the traditional action classification method of manually selecting features and the action classification method based on deep learning, focusing on different deep learning methods including convolutional neural networks, recurrent neural network, dual-stream network, long and short-term memory network, etc., and it summarizes the commonly used video action classification data sets, summarizes and prospects the development of video action classification methods.

Key words : video action classification；data set；deep learning