Jisi A.1 and Shoulin Yin This email address is being protected from spambots. You need JavaScript enabled to view it.1,2

1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150000 China
2Software College, Shenyang Normal University, Shenyang 110034, China


 

Received: September 20, 2020
Accepted: October 3, 2020
Publication Date: April 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202104_24(2).0002  


ABSTRACT


Behavior recognition is a research hotspot in the field of computer vision and it also is a challenging task. In particular, student behavior analysis has an impact on the efficiency of classroom education. Aiming at the complex student behavior recognition problem in the video, we propose a new feature fusion network for student behavior recognition in education in this paper. The new feature fusion network contains two main stages: feature extraction and classification. First, we combine spatial affine transformation network with convolutional neural network to extract more detailed features. Then, the weighted sum method is adopted to fuse the spatial-temporal features, and the softmax classifier is improved for classification recognition to improve the final recognition result. Experiments are carried out on standard human behavior data HMDB51, UCF101 and real student behavior data. The results show that the proposed algorithm can achieve better recognition effect than other state-of-the-art recognition algorithms.


Keywords: Behavior recognition; feature fusion; spatial affine transformation; spatial-temporal feature


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