Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 139-147.doi: 10.11896/JsJkx.190900176

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Review of Deep Learning-based Action Recognition Algorithms

HE Lei, SHAO Zhan-peng, ZHANG Jian-hua and ZHOU Xiao-long   

  1. College of Computer Science and Technology,ZheJiang University of Technology,Hangzhou 310023,China
  • Published:2020-07-07
  • About author:HE Lei, born in 1994, master.His main research interests include image processing and action recognition.
    SHAO Zhan-peng, Ph.D, is a member of China Computer Federation.His research interests include action recognition and pose estimation.
  • Supported by:
    This work was supported the National Natural Science Foundation of China (20160283,61603341) and Natural Science Foundation of ZheJiang Province,China (KYY-ZX-20190013,KYY-ZX-20180114).

Abstract: Action recognition is one of the fundamental problems in the field of computer vision.Currently,deep learning-based method is one of the mainstream methods for action recognition.In the existing researches,the traditional feature extraction method generally manually designs features that can represent video actions.However,this method usually requires a particular model to classify features,which cannot achieve high performance in real applications,while the introduction of deep learning brings a new development direction for action recognition.This paper briefly reviews on the action recognition methods based on deep learning.Firstly,the research background and significance of action recognition are introduced,and the traditional methods and deep learning-based methods are surveyed respectively.Then,the model architectures of three algorithms based on deep learning are classified and introduced,namely Two-Stream network,3DConvNet,CNN-LSTM network.Finally,the common used public validation datasets are introduced,and horizontal comparison is carried out on the recognition algorithms based on two data modes.Among these datasets,they can be grouped into two categories,RGB-based (e.g.,UCF101,HMDB51) and skeleton-based datasets (e.g.,NTU RGB+D).Experimental results show that the deep learning-based methods have made great advances,and the application of convolutional neural network has greatly promoted the development of action recognition algorithm.They gradually replace the traditional method based on manual features extraction.For RGB-based action recognition,Two-Stream and 3DConvNet are currently state-of-the-art methods.For skeleton-based action recognition,Two-Stream and spatiotemporal graph network achieve the best performance.

Key words: 3D-ConvNet, Action recognition, Convolutional neural network, Deep learning, Recurrent neural network

CLC Number: 

  • TP391.4
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