面向视频中人体行为识别的复合型深度神经网络

doi:10.16451/j.cnki.issn1003-6059.202206008

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Abstract Aiming at the deficiencies of 3D convolutional neural network and two-stream convolutional neural network for human activities recognition in video, a composite deep neural network combining two-stream convolutional network and 3D convolutional network is proposed. The improved residual(2+1)D convolutional neural network is utilized in both the temporal sub-network and the spatial sub-network of two-stream architecture. Behavioral representation and classification methods are learned from RGB and optical flow of video, respectively. The classification results of temporal stream and spatial stream sub-networks are combined. Furthermore, in the process of network training, stochastic gradient descent with the momentum improved by gradient centralization algorithm is proposed to improve the network generalization performance without varying the network structure. Experimental results show that the proposed network achieves higher accuracy on UCF101 and HMDB51.

Key words： Human Activity Recognition Two-Stream Convolutional Network 3D Convolution Neural Network Gradient Centralization

Received: 08 March 2022

ZTFLH:

TP 391.41

Corresponding Authors: QIAN Huimin, Ph.D., associate professor. Her research interests include computer vision and machine learning.

About author:: HUANG Min, master student. Her research interests include human activity recognition.
SHANG Ruixing, master student. Her research interests include human activity recognition.

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	HUANG Min
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Cite this article:

HUANG Min,SHANG Ruixing,QIAN Huimin. Composite Deep Neural Network for Human Activities Recognition in Video[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(6): 562-570.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202206008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I6/562

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